diff --git a/lzero/mcts/buffer/game_buffer_muzero.py b/lzero/mcts/buffer/game_buffer_muzero.py
index 2ba8180de..cf0ce4cde 100644
--- a/lzero/mcts/buffer/game_buffer_muzero.py
+++ b/lzero/mcts/buffer/game_buffer_muzero.py
@@ -734,7 +734,15 @@ def _compute_target_policy_non_reanalyzed(
                             policy_tmp = [0 for _ in range(policy_shape)]
                             for index, legal_action in enumerate(legal_actions[policy_index]):
                                 # only the action in ``legal_action`` the policy logits is nonzero
-                                policy_tmp[legal_action] = distributions[index]
+                                #breakpoint()
+                                #print(f"len(distributions)={len(distributions)}, len(legal_actions[policy_index])={len(legal_actions[policy_index])}")                
+                                #if len(distributions) != len(legal_actions[policy_index]):
+                                #    breakpoint()
+                                # Temporary fix: might be masking underlying error
+                                if index < len(distributions):
+                                    policy_tmp[legal_action] = distributions[index]
+                                else:
+                                    policy_tmp[legal_action] = 0
                             target_policies.append(policy_tmp)
                     else:
                         # NOTE: the invalid padding target policy, O is to make sure the corresponding cross_entropy_loss=0
diff --git a/lzero/policy/alphazero.py b/lzero/policy/alphazero.py
index e87c3bd5f..b12162afc 100644
--- a/lzero/policy/alphazero.py
+++ b/lzero/policy/alphazero.py
@@ -264,7 +264,11 @@ def _forward_collect(self, obs: Dict, temperature: float = 1) -> Dict[str, torch
                                                                   init_state=init_state[env_id],
                                                                   katago_policy_init=False,
                                                                   katago_game_state=katago_game_state[env_id]))
-            action, mcts_probs, root = self._collect_mcts.get_next_action(state_config_for_simulation_env_reset, self._policy_value_fn, self.collect_mcts_temperature, True)
+            #breakpoint()
+            action, mcts_probs = self._collect_mcts.get_next_action(state_config_for_simulation_env_reset, self._policy_value_fn, self.collect_mcts_temperature, True)
+
+            # Kev: uncommented this
+            #action, mcts_probs, root = self._collect_mcts.get_next_action(state_config_for_simulation_env_reset, self._policy_value_fn, self.collect_mcts_temperature, True)
 
             output[env_id] = {
                 'action': action,
diff --git a/zoo/custom_envs/equation_solver/__init__.py b/zoo/custom_envs/equation_solver/__init__.py
new file mode 100644
index 000000000..654887437
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/__init__.py
@@ -0,0 +1,3 @@
+from .env_single_eqn import singleEqn
+from .env_single_eqn_easy import singleEqnEasy
+
diff --git a/zoo/custom_envs/equation_solver/config_muzero_single_eqn.py b/zoo/custom_envs/equation_solver/config_muzero_single_eqn.py
new file mode 100644
index 000000000..c2c336701
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/config_muzero_single_eqn.py
@@ -0,0 +1,89 @@
+# ==============================================================
+# Kev: Adapted from lunarlander_disc_muzero_config
+# ==============================================================
+
+
+from easydict import EasyDict
+from lzero.entry import train_muzero
+
+
+# ==============================================================
+# begin of the most frequently changed config specified by the user
+# ==============================================================
+collector_env_num = 8
+n_episode = 8
+evaluator_env_num = 3
+num_simulations = 100
+update_per_collect = 200
+batch_size = 256
+max_env_step = int(1e5)
+reanalyze_ratio = 0.0
+
+# ==============================================================
+# end of the most frequently changed config specified by the user
+# ==============================================================
+
+single_eqn_muzero_config = dict(
+    exp_name=f'data_muzero/x+b',
+    env=dict(
+        env_name='singleEqn_env',  # Changed from LunarLander-v2
+        continuous=False,
+        manually_discretization=False,
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        n_evaluator_episode=evaluator_env_num,
+        manager=dict(shared_memory=False, ),
+    ),
+    policy=dict(
+        model=dict(
+            observation_shape=41,  # Changed from 8
+            action_space_size=50,  # Changed from 4
+            model_type='mlp',
+            lstm_hidden_size=128,
+            latent_state_dim=128,
+            self_supervised_learning_loss=True,
+            discrete_action_encoding_type='not_one_hot',
+            res_connection_in_dynamics=True,
+            norm_type='BN',
+        ),
+        model_path=None,
+        cuda=True,
+        env_type='not_board_games',
+        action_type= "varied_action_space",
+        game_segment_length=10,
+        update_per_collect=update_per_collect,
+        batch_size=batch_size,
+        optim_type='Adam',
+        piecewise_decay_lr_scheduler=False,
+        learning_rate=0.001,
+        ssl_loss_weight=2,
+        grad_clip_value=0.5,
+        num_simulations=num_simulations,
+        reanalyze_ratio=reanalyze_ratio,
+        n_episode=n_episode,
+        eval_freq=int(1e3),
+        replay_buffer_size=int(1e6),
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+    ),
+)
+single_eqn_muzero_config = EasyDict(single_eqn_muzero_config)
+main_config = single_eqn_muzero_config
+
+single_eqn_muzero_create_config = dict(
+    env=dict(
+        type='singleEqn_env',  # Changed from lunarlander
+        import_names=['zoo.custom_envs.equation_solver.env_single_eqn'],  # Changed from lunarlander path
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(
+        type='muzero',
+        import_names=['lzero.policy.muzero'],
+    ),
+)
+single_eqn_muzero_create_config = EasyDict(single_eqn_muzero_create_config)
+create_config = single_eqn_muzero_create_config
+
+if __name__ == "__main__":
+    seed = 14850
+    train_muzero([main_config, create_config], seed=seed, model_path=main_config.policy.model_path, max_env_step=max_env_step)
\ No newline at end of file
diff --git a/zoo/custom_envs/equation_solver/config_muzero_single_eqn_easy.py b/zoo/custom_envs/equation_solver/config_muzero_single_eqn_easy.py
new file mode 100644
index 000000000..53c2ea05f
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/config_muzero_single_eqn_easy.py
@@ -0,0 +1,87 @@
+# ==============================================================
+# Kev: Adapted from lunarlander_disc_muzero_config
+# ==============================================================
+
+from easydict import EasyDict
+from lzero.entry import train_muzero
+
+
+# ==============================================================
+# begin of the most frequently changed config specified by the user
+# ==============================================================
+collector_env_num = 8
+n_episode = 8
+evaluator_env_num = 3
+num_simulations = 1
+update_per_collect = 100
+batch_size = 128
+max_env_step = int(1e5)
+reanalyze_ratio = 0.2
+
+# ==============================================================
+# end of the most frequently changed config specified by the user
+# ==============================================================
+
+single_eqn_muzero_config = dict(
+    exp_name=f'data_muzero/x+b',
+    env=dict(
+        env_name='singleEqnEasy_env',  # Changed from LunarLander-v2
+        continuous=False,
+        manually_discretization=False,
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        n_evaluator_episode=evaluator_env_num,
+        manager=dict(shared_memory=False, ),
+    ),
+    policy=dict(
+        model=dict(
+            observation_shape=41,  
+            action_space_size=4, 
+            model_type='mlp',
+            latent_state_dim=32,
+            self_supervised_learning_loss=False,
+            discrete_action_encoding_type='not_one_hot',
+            res_connection_in_dynamics=False,
+            norm_type='BN',
+        ),
+        model_path=None,
+        cuda=True,
+        env_type='not_board_games',
+        action_type= "fixed_action_space",
+        game_segment_length=2,
+        update_per_collect=update_per_collect,
+        batch_size=batch_size,
+        optim_type='Adam',
+        piecewise_decay_lr_scheduler=False,
+        learning_rate=0.001,
+        ssl_loss_weight=1,
+        grad_clip_value=1.0,
+        num_simulations=num_simulations,
+        reanalyze_ratio=reanalyze_ratio,
+        n_episode=n_episode,
+        eval_freq=int(1e3),
+        replay_buffer_size=int(1e4),
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+    ),
+)
+single_eqn_muzero_config = EasyDict(single_eqn_muzero_config)
+main_config = single_eqn_muzero_config
+
+single_eqn_muzero_create_config = dict(
+    env=dict(
+        type='singleEqnEasy_env', 
+        import_names=['zoo.custom_envs.equation_solver.env_single_eqn_easy'],
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(
+        type='muzero',
+        import_names=['lzero.policy.muzero'],
+    ),
+)
+single_eqn_muzero_create_config = EasyDict(single_eqn_muzero_create_config)
+create_config = single_eqn_muzero_create_config
+
+if __name__ == "__main__":
+    seed = 14850
+    train_muzero([main_config, create_config], seed=seed, model_path=main_config.policy.model_path, max_env_step=max_env_step)
\ No newline at end of file
diff --git a/zoo/custom_envs/equation_solver/config_single_eqn.py b/zoo/custom_envs/equation_solver/config_single_eqn.py
new file mode 100644
index 000000000..f7d30b8c1
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/config_single_eqn.py
@@ -0,0 +1,110 @@
+from my_train_alphazero import my_train_alphazero
+from easydict import EasyDict
+
+# ==============================================================
+# Frequently changed config specified by the user (lightweight settings)
+# ==============================================================
+collector_env_num = 4         # Number of parallel environments for data collection
+n_episode = 4                 # Number of episodes per training iteration
+evaluator_env_num = 1         # Number of evaluator environments
+num_simulations = 50          # MCTS simulations per move (try increasing if needed)
+update_per_collect = 100      # Number of gradient updates per data collection cycle
+batch_size = 32               # Mini-batch size for training
+max_env_step = int(1e3)       # Maximum total environment steps for a quick run
+model_path = None
+mcts_ctree = False
+
+# ==============================================================
+# Configurations for singleEqn_env (lightweight version)
+# ==============================================================
+singleEqn_alphazero_config = dict(
+    exp_name='data_alphazero/singleEqn/x+b/',
+    env=dict(
+        battle_mode='play_with_bot_mode',
+        battle_mode_in_simulation_env='self_play_mode',  # For simulation during MCTS
+        channel_last=False,
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        n_evaluator_episode=evaluator_env_num,
+        manager=dict(shared_memory=False),
+        agent_vs_human=False,
+        prob_random_agent=0,
+        prob_expert_agent=0,
+        prob_random_action_in_bot=0,
+        scale=True,
+        render_mode=None,
+        replay_path=None,
+        alphazero_mcts_ctree=mcts_ctree,
+    ),
+    policy=dict(
+        mcts_ctree=mcts_ctree,
+        simulation_env_id='singleEqn_env',  # Must match the registered name of your environment
+        model=dict(
+            type='AlphaZeroMLPModel',
+            import_names=['zoo.custom_envs.equation_solver.my_alphazero_mlp_model'],
+            observation_shape=(41,),        # Flat vector of length 41
+            action_space_size=50,             # Matches your environment's action_dim
+            hidden_sizes=[64, 64],          # MLP hidden layer sizes
+        ),
+        cuda=True,
+        env_type='not_board_games',
+        action_type='varied_action_space',
+        update_per_collect=update_per_collect,
+        batch_size=batch_size,
+        optim_type='Adam',
+        lr_piecewise_constant_decay=False,
+        # learning_rate=0.003,
+        learning_rate=3e-4,
+        grad_clip_value=0.5,
+        value_weight=1.0,
+        entropy_weight=0.0,
+        n_episode=n_episode,
+        eval_freq=int(2e3),
+        mcts=dict(num_simulations=num_simulations),
+        collector_env_num=collector_env_num,
+        evaluator_env_num=evaluator_env_num,
+        other=dict(
+            replay_buffer=dict(
+                type='advanced',              # Use advanced (or prioritized) replay buffer
+                replay_buffer_size=10000,       # Set a smaller buffer for lightweight runs
+                sample_min_limit_ratio=0.25,      # Allow sampling even if only 50% of batch size is available.
+                alpha=0.6,
+                beta=0.4,
+                anneal_step=100000,
+                enable_track_used_data=False,
+                deepcopy=False,
+                save_episode=False,
+            )
+        ),
+    ),
+)
+singleEqn_alphazero_config = EasyDict(singleEqn_alphazero_config)
+main_config = singleEqn_alphazero_config
+
+singleEqn_alphazero_create_config = dict(
+    env=dict(
+        type='singleEqn_env',
+        import_names=['zoo.custom_envs.equation_solver.env_single_eqn'],  # Adjust this path if needed
+    ),
+    env_manager=dict(type='subprocess'),
+    policy=dict(
+        type='MyAlphaZeroPolicy',  # Your custom policy subclass
+        import_names=['zoo.custom_envs.equation_solver.my_alphazero_policy'],
+    ),
+    collector=dict(
+        type='episode_alphazero',
+        import_names=['lzero.worker.alphazero_collector'],
+    ),
+    evaluator=dict(
+        type='alphazero',
+        import_names=['lzero.worker.alphazero_evaluator'],
+    )
+)
+singleEqn_alphazero_create_config = EasyDict(singleEqn_alphazero_create_config)
+create_config = singleEqn_alphazero_create_config
+
+if __name__ == '__main__':
+    from lzero.entry import train_alphazero
+    # Merge the environment configuration into the policy config.
+    main_config.policy.env = main_config.env
+    my_train_alphazero([main_config, create_config], seed=0, model_path=model_path, max_env_step=max_env_step)
diff --git a/zoo/custom_envs/equation_solver/env_single_eqn.py b/zoo/custom_envs/equation_solver/env_single_eqn.py
new file mode 100644
index 000000000..e87ae3031
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/env_single_eqn.py
@@ -0,0 +1,322 @@
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import logging
+import numpy as np
+from sympy import sympify, symbols
+from gymnasium import spaces
+from operator import add, sub, mul, truediv
+
+# Import custom helper functions (assumed available)
+from utils_custom_functions import *
+from utils_env import *
+
+# LightZero compatibility
+from ding.envs import BaseEnv, BaseEnvTimestep
+from ding.utils import ENV_REGISTRY
+from easydict import EasyDict
+
+logger = logging.getLogger(__name__)
+
+@ENV_REGISTRY.register('singleEqn_env')
+class singleEqn(BaseEnv):
+    """Environment for solving simple algebraic equations using RL in a LightZero‐compatible format."""
+
+    config = dict(
+        env_id="singleEqn-v0",
+        battle_mode='self_play_mode',
+        battle_mode_in_simulation_env='self_play_mode',
+        render_mode=None,
+        replay_path=None,
+        bot_action_type=None,
+        agent_vs_human=False,
+        prob_random_agent=0,
+        prob_expert_agent=0,
+        prob_random_action_in_bot=0.0,
+        scale=False,
+        stop_value=None    
+        )
+
+    metadata = {"render_modes": ["human"]}
+
+    def __init__(self, env_fn=None, cfg=None, main_eqn='x+b', state_rep='integer_1d', normalize_rewards=True, cache=False, \
+        verbose=False):
+        #super().__init__(env_fn, cfg)
+        self.cfg = EasyDict(cfg or self.config)
+        self.battle_mode = self.cfg.get('battle_mode', 'self_play_mode')
+        self.battle_mode_in_simulation_env = self.cfg.get('battle_mode_in_simulation_env', 'self_play_mode')
+        
+        # Parameters from configuration
+        self.max_expr_length = 20
+        self.max_steps = 10
+        self.action_dim = 50
+        self.observation_dim = 2 * self.max_expr_length + 1
+
+        # Reward settings
+        self.reward_solved = 100
+        self.reward_invalid_equation = -100
+        self.reward_illegal_action = -100
+        self.episode_return = 0
+
+        # Options
+        self.normalize_rewards = normalize_rewards
+        self.state_rep = state_rep
+        self.verbose = verbose
+        self.cache = cache
+        if self.cache:
+            self.action_cache = {}  # Cache for dynamic actions
+
+        # Set up the equation and symbols
+        self.main_eqn = sympify(main_eqn)
+        self.lhs = self.main_eqn
+        self.rhs = 0
+        self.x = symbols('x')
+
+        # Build feature dictionary and initial fixed actions
+        self.setup()
+
+        # Initial state vector
+        obs, _ = self.to_vec(self.lhs, self.rhs)
+        self.obs = np.array(obs, dtype=np.float32)  #to work with muzero
+        self.current_steps = 0
+
+        # Define action and observation spaces (LightZero expects these as properties)
+        self._action_space = spaces.Discrete(self.action_dim)
+        self._reward_space = spaces.Box(low=self.reward_invalid_equation, high=self.reward_solved, shape=(1,), dtype=np.float32)
+        if state_rep == 'integer_1d':
+            self._observation_space = spaces.Box(low=-np.inf, high=np.inf, 
+                                                  shape=(self.observation_dim,), dtype=np.float32)
+        elif state_rep == 'integer_2d':
+            self._observation_space = spaces.Box(low=-np.inf, high=np.inf, 
+                                                  shape=(self.observation_dim, 2), dtype=np.float32)
+        elif state_rep in ['graph_integer_1d', 'graph_integer_2d']:
+            self._observation_space = spaces.Dict({
+                "node_features": spaces.Box(low=-np.inf, high=np.inf, 
+                                            shape=(self.observation_dim, 2), dtype=np.float32),
+                "edge_index": spaces.Box(low=0, high=self.observation_dim, 
+                                         shape=(2, 2*self.observation_dim), dtype=np.int32),
+                "node_mask": spaces.Box(low=0, high=1, 
+                                        shape=(self.observation_dim,), dtype=np.int32),
+                "edge_mask": spaces.Box(low=0, high=1, 
+                                        shape=(2*self.observation_dim,), dtype=np.int32),
+            })
+        else:
+            raise ValueError(f"Unsupported state representation: {state_rep}")
+
+    def setup(self):
+        # Build feature dictionary (e.g., {'add': -1, 'x': 1, 'a':2, ...})
+        self.feature_dict = make_feature_dict(self.main_eqn, self.state_rep)
+
+        # Define fixed actions (operations paired with a term)
+        self.actions_fixed = [
+            # (custom_expand, None),
+            # (custom_factor, None),
+            # (custom_collect, self.x), 
+            # (custom_together, None),
+            # (custom_ratsimp, None),
+            # (custom_square, None),
+            # (custom_sqrt, None),
+            (mul, -1)
+        ]
+        # Generate dynamic actions based on the current equation
+        if self.cache:
+            self.actions, self.action_mask = make_actions_cache(self.lhs, self.rhs,
+                                                                  self.actions_fixed,
+                                                                  self.action_dim,
+                                                                  self.action_cache)
+        else:
+            self.actions, self.action_mask = make_actions(self.lhs, self.rhs,
+                                                           self.actions_fixed,
+                                                           self.action_dim)
+
+    def step(self, action_index: int) -> BaseEnvTimestep:
+        # Recompute dynamic actions since they depend on the current state
+        lhs_old, rhs_old = self.lhs, self.rhs
+
+        # Double check mask
+        if action_index not in self.legal_actions:
+            print(f'\nIllegal action taken: action_index = {action_index}')
+            #print(f'\nIllegal action taken: action_index = {self.action_mask}\n')
+            legal_actions_temp = [i for i, valid in enumerate(self.action_mask) if valid]
+            action_index = np.random.choice(legal_actions_temp)
+
+        # Apply the selected action
+        action = self.actions[action_index]
+        operation, term = action
+        lhs_new, rhs_new = operation(lhs_old, term), operation(rhs_old, term)
+        obs_new, _ = self.to_vec(lhs_new, rhs_new)
+
+        # Validate the new equation and check for a solved state
+        is_valid_eqn, lhs_new, rhs_new = check_valid_eqn(lhs_new, rhs_new)
+        is_solved = check_eqn_solved(lhs_new, rhs_new, self.main_eqn)
+
+
+
+        # Compute reward based on equation complexity changes
+        reward = self.find_reward(lhs_old, rhs_old, lhs_new, rhs_new, is_valid_eqn, is_solved)
+        self.episode_return += reward
+
+        # Temp: remove soon.
+        if term == -1 and not is_solved:
+            reward = 0
+
+        # Termination conditions: solved, exceeded max steps, or invalid equation
+        too_many_steps = self.current_steps >= self.max_steps
+        done = bool(is_solved or too_many_steps or not is_valid_eqn)
+        truncated = False
+
+        # Update state and step counter
+        self.lhs, self.rhs, self.obs = lhs_new, rhs_new, np.array(obs_new, dtype=np.float32)
+        self.current_steps += 1
+
+        # Update actions
+        self.actions, self.action_mask  = make_actions(lhs_new, rhs_new, self.actions_fixed, self.action_dim)
+
+        info = {
+            'is_solved': is_solved,
+            'is_valid_eqn': is_valid_eqn,
+            'too_many_steps': too_many_steps,
+            'action_mask': self.action_mask
+        }
+
+        if done:
+            info['eval_episode_return'] = self.episode_return
+
+        verbose = True
+        if verbose:
+            #print(f'{self.lhs} = {self.rhs}. (Operation, term): {operation_names.get(operation, operation)}, {term} | reward = {reward:.2f}')
+            print(f'(Operation, term): {operation_names.get(operation, operation)}, {term} | reward = {reward:.2f}')
+            if operation_names.get(operation, operation) == 'multiply' and reward > 0:
+                print(f'\n\n{lhs_old} = {rhs_old} => {lhs_new} = {rhs_new}\n\n' )
+
+            if is_solved:
+                print(f'\nSOLVED: {self.lhs} = {self.rhs}\n')
+
+        lightzero_obs_dict = {
+            'observation': np.array(obs_new, dtype=np.float32),
+            'action_mask': self.action_mask,
+            'board': np.array(obs_new, dtype=np.float32),  
+            'current_player_index': 0,  #
+            'to_play': -1
+        }
+        return BaseEnvTimestep(lightzero_obs_dict, reward, done, info)
+
+    def reset(self, seed=0, options=None, **kwargs):
+        # You can optionally capture start_player_index if needed:
+        start_player_index = kwargs.get('start_player_index', 0)
+        # Then proceed with the reset
+        self.current_steps = 0
+        self.lhs, self.rhs = self.main_eqn, 0
+        self.actions, self.action_mask  = make_actions(self.lhs, self.rhs, self.actions_fixed, self.action_dim)
+        obs, _ = self.to_vec(self.lhs, self.rhs)
+        self.obs = np.array(obs, dtype=np.float32)
+        self.episode_return = 0
+        lightzero_obs_dict = {
+            'observation': obs,
+            'board': obs,  # for compatibility
+            'action_mask': self.action_mask,
+            'to_play': -1,
+            'current_player_index': start_player_index 
+        }
+        return lightzero_obs_dict
+
+
+    def render(self, mode: str = "human"):
+        print(f'{self.lhs} = {self.rhs}')
+
+    def to_vec(self, lhs, rhs):
+        if self.state_rep == 'integer_1d':
+            return integer_encoding_1d(lhs, rhs, self.feature_dict, self.max_expr_length)
+        elif self.state_rep == 'integer_2d':
+            return integer_encoding_2d(lhs, rhs, self.feature_dict, self.max_expr_length)
+        elif self.state_rep in ['graph_integer_1d', 'graph_integer_2d']:
+            return graph_encoding(lhs, rhs, self.feature_dict, self.max_expr_length)
+        else:
+            raise ValueError(f"Unknown state representation: {self.state_rep}")
+
+    def find_reward(self, lhs_old, rhs_old, lhs_new, rhs_new, is_valid_eqn, is_solved):
+        if not is_valid_eqn:
+            reward = self.reward_invalid_equation
+        elif is_solved:
+            reward = self.reward_solved
+        else:
+            obs_old_complexity = get_complexity_expression(lhs_old) + get_complexity_expression(rhs_old)
+            obs_new_complexity = get_complexity_expression(lhs_new) + get_complexity_expression(rhs_new)
+            reward = obs_old_complexity - obs_new_complexity
+
+        if self.normalize_rewards:
+            max_reward, min_reward = self.reward_solved, self.reward_invalid_equation
+            reward = 2 * (reward - min_reward) / (max_reward - min_reward) - 1
+        return reward
+
+    def get_valid_action_mask(self):
+        return self.action_mask
+
+    def current_state(self):
+        raw_obs = self.obs.astype(np.float32)
+        return raw_obs, raw_obs
+
+
+    def get_done_winner(self):
+        """
+        Returns:
+            A tuple (done, winner) where:
+            - done (bool): True if the episode is over (e.g., solved, invalid, or max steps reached).
+            - winner (int): In single-agent tasks, you can return 0 if solved, or -1 otherwise.
+        """
+        is_solved = check_eqn_solved(self.lhs, self.rhs, self.main_eqn)
+        if is_solved:
+            return True, 0
+        else:
+            return False, -1
+
+    @property
+    def current_player(self):
+        return 0
+
+
+    @property
+    def legal_actions(self):
+        # Return indices where the action mask is True.
+        return [i for i, valid in enumerate(self.action_mask) if valid]
+
+
+    def close(self):
+        """
+        Clean up resources.
+        """
+        self._init_flag = False
+
+    def seed(self, seed: int = 0, dynamic_seed: bool = True):
+        """
+        Set the random seed for the environment.
+        """
+        np.random.seed(seed)
+
+    def __repr__(self):
+        """
+        Return a string representation of the environment.
+        """
+        return f"singleEqn(main_eqn={self.main_eqn}"
+
+    @property
+    def observation_space(self):
+        """
+        Return the observation space.
+        """
+        return self._observation_space
+
+    @property
+    def action_space(self):
+        """
+        Return the action space.
+        """
+        return self._action_space
+
+    @property
+    def reward_space(self):
+        """
+        Return the reward space.
+        """
+        return self._reward_space
diff --git a/zoo/custom_envs/equation_solver/env_single_eqn_easy.py b/zoo/custom_envs/equation_solver/env_single_eqn_easy.py
new file mode 100644
index 000000000..4af5192fc
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/env_single_eqn_easy.py
@@ -0,0 +1,292 @@
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import logging
+import numpy as np
+from sympy import sympify, symbols
+from gymnasium import spaces
+from operator import add, sub, mul, truediv
+
+# Import custom helper functions (assumed available)
+from utils_custom_functions import *
+from utils_env import *
+
+# LightZero compatibility
+from ding.envs import BaseEnv, BaseEnvTimestep
+from ding.utils import ENV_REGISTRY
+from easydict import EasyDict
+
+logger = logging.getLogger(__name__)
+
+@ENV_REGISTRY.register('singleEqnEasy_env')
+class singleEqnEasy(BaseEnv):
+    """Environment for solving simple algebraic equations using RL in a LightZero‐compatible format."""
+
+    config = dict(
+        env_id="singleEqnEasy-v0",
+        battle_mode='self_play_mode',
+        battle_mode_in_simulation_env='self_play_mode',
+        render_mode=None,
+        replay_path=None,
+        bot_action_type=None,
+        agent_vs_human=False,
+        prob_random_agent=0,
+        prob_expert_agent=0,
+        prob_random_action_in_bot=0.0,
+        scale=False,
+        stop_value=None    
+        )
+
+    metadata = {"render_modes": ["human"]}
+
+    def __init__(self, env_fn=None, cfg=None, main_eqn='x+b', state_rep='integer_1d', normalize_rewards=True, cache=False, \
+        verbose=False):
+        #super().__init__(env_fn, cfg)
+        self.cfg = EasyDict(cfg or self.config)
+        self.battle_mode = self.cfg.get('battle_mode', 'self_play_mode')
+        self.battle_mode_in_simulation_env = self.cfg.get('battle_mode_in_simulation_env', 'self_play_mode')
+        
+        # Parameters from configuration
+        self.max_expr_length = 20
+        self.max_steps = 2
+        self.action_dim = 4
+        self.observation_dim = 2 * self.max_expr_length + 1
+
+        # Reward settings
+        self.reward_solved = 100
+        self.reward_invalid_equation = -100
+        self.reward_illegal_action = -100
+        self.episode_return = 0
+
+        # Options
+        self.normalize_rewards = normalize_rewards
+        self.state_rep = state_rep
+        self.verbose = verbose
+        self.cache = cache
+        if self.cache:
+            self.action_cache = {}  # Cache for dynamic actions
+
+        # Set up the equation and symbols
+        self.main_eqn = sympify(main_eqn)
+        self.lhs = self.main_eqn
+        self.rhs = 0
+        self.x = symbols('x')
+
+        # Build feature dictionary and initial fixed actions
+        self.setup()
+
+        # Initial state vector
+        obs, _ = self.to_vec(self.lhs, self.rhs)
+        self.obs = np.array(obs, dtype=np.float32)  #to work with muzero
+        self.current_steps = 0
+
+        # Define action and observation spaces (LightZero expects these as properties)
+        self._action_space = spaces.Discrete(self.action_dim)
+        self._reward_space = spaces.Box(low=self.reward_invalid_equation, high=self.reward_solved, shape=(1,), dtype=np.float32)
+        if state_rep == 'integer_1d':
+            self._observation_space = spaces.Box(low=-np.inf, high=np.inf, 
+                                                  shape=(self.observation_dim,), dtype=np.float32)
+        elif state_rep == 'integer_2d':
+            self._observation_space = spaces.Box(low=-np.inf, high=np.inf, 
+                                                  shape=(self.observation_dim, 2), dtype=np.float32)
+        elif state_rep in ['graph_integer_1d', 'graph_integer_2d']:
+            self._observation_space = spaces.Dict({
+                "node_features": spaces.Box(low=-np.inf, high=np.inf, 
+                                            shape=(self.observation_dim, 2), dtype=np.float32),
+                "edge_index": spaces.Box(low=0, high=self.observation_dim, 
+                                         shape=(2, 2*self.observation_dim), dtype=np.int32),
+                "node_mask": spaces.Box(low=0, high=1, 
+                                        shape=(self.observation_dim,), dtype=np.int32),
+                "edge_mask": spaces.Box(low=0, high=1, 
+                                        shape=(2*self.observation_dim,), dtype=np.int32),
+            })
+        else:
+            raise ValueError(f"Unsupported state representation: {state_rep}")
+
+    def setup(self):
+        # Build feature dictionary (e.g., {'add': -1, 'x': 1, 'a':2, ...})
+        self.feature_dict = make_feature_dict(self.main_eqn, self.state_rep)
+
+        a, b = symbols('a b')
+        operations = [add, sub, mul, truediv]
+        terms = [b]
+        self.actions_fixed = []
+        self.actions = list(product(operations, terms))
+        self.action_mask = [True for i in self.actions]
+        #print(f'actions = {self.actions}')
+
+
+    def step(self, action_index: int) -> BaseEnvTimestep:
+
+        # Recompute dynamic actions since they depend on the current state
+        lhs_old, rhs_old = self.lhs, self.rhs
+
+        # Apply the selected action
+        action = self.actions[action_index]
+        operation, term = action
+        lhs_new, rhs_new = operation(lhs_old, term), operation(rhs_old, term)
+        obs_new, _ = self.to_vec(lhs_new, rhs_new)
+
+        # Validate the new equation and check for a solved state
+        is_valid_eqn, lhs_new, rhs_new = check_valid_eqn(lhs_new, rhs_new)
+        is_solved = check_eqn_solved(lhs_new, rhs_new, self.main_eqn)
+
+        # Compute reward based on equation complexity changes
+        reward = self.find_reward(lhs_old, rhs_old, lhs_new, rhs_new, is_valid_eqn, is_solved)
+        self.episode_return += reward
+
+        # Termination conditions: solved, exceeded max steps, or invalid equation
+        too_many_steps = self.current_steps >= self.max_steps
+        done = bool(is_solved or too_many_steps or not is_valid_eqn)
+        truncated = False
+
+        # Update state and step counter
+        self.lhs, self.rhs, self.obs = lhs_new, rhs_new, np.array(obs_new, dtype=np.float32)
+        self.current_steps += 1
+
+        info = {
+            'is_solved': is_solved,
+            'is_valid_eqn': is_valid_eqn,
+            'too_many_steps': too_many_steps,
+            'action_mask': self.action_mask
+        }
+
+        if done:
+            info['eval_episode_return'] = self.episode_return
+
+        verbose = True
+        if verbose:
+            #print(f'\nStep: {self.current_steps}: {lhs_old} = {rhs_old} => {lhs_new} = {rhs_new}')
+            print(f'(Operation, term): {operation_names.get(operation, operation)}, {term} | reward = {reward:.2f}\n')
+
+            if is_solved:
+                print(f'\nSOLVED: {self.lhs} = {self.rhs}\n')
+
+        lightzero_obs_dict = {
+            'observation': np.array(obs_new, dtype=np.float32),
+            'action_mask': self.action_mask,
+            'board': np.array(obs_new, dtype=np.float32),  
+            'current_player_index': 0,  #
+            'to_play': -1
+        }
+        return BaseEnvTimestep(lightzero_obs_dict, reward, done, info)
+
+    def reset(self, seed=0, options=None, **kwargs):
+        # You can optionally capture start_player_index if needed:
+        start_player_index = kwargs.get('start_player_index', 0)
+        # Then proceed with the reset
+        self.current_steps = 0
+        self.lhs, self.rhs = self.main_eqn, 0
+        #self.actions, self.action_mask  = make_actions(self.lhs, self.rhs, self.actions_fixed, self.action_dim)
+        obs, _ = self.to_vec(self.lhs, self.rhs)
+        self.obs = np.array(obs, dtype=np.float32)
+        self.episode_return = 0
+        lightzero_obs_dict = {
+            'observation': obs,
+            'board': obs,  # for compatibility
+            'action_mask': self.action_mask,
+            'to_play': -1,
+            'current_player_index': start_player_index 
+        }
+        return lightzero_obs_dict
+
+
+    def render(self, mode: str = "human"):
+        print(f'{self.lhs} = {self.rhs}')
+
+    def to_vec(self, lhs, rhs):
+        if self.state_rep == 'integer_1d':
+            return integer_encoding_1d(lhs, rhs, self.feature_dict, self.max_expr_length)
+        elif self.state_rep == 'integer_2d':
+            return integer_encoding_2d(lhs, rhs, self.feature_dict, self.max_expr_length)
+        elif self.state_rep in ['graph_integer_1d', 'graph_integer_2d']:
+            return graph_encoding(lhs, rhs, self.feature_dict, self.max_expr_length)
+        else:
+            raise ValueError(f"Unknown state representation: {self.state_rep}")
+
+    def find_reward(self, lhs_old, rhs_old, lhs_new, rhs_new, is_valid_eqn, is_solved):
+        if not is_valid_eqn:
+            reward = self.reward_invalid_equation
+        elif is_solved:
+            reward = self.reward_solved
+        else:
+            obs_old_complexity = get_complexity_expression(lhs_old) + get_complexity_expression(rhs_old)
+            obs_new_complexity = get_complexity_expression(lhs_new) + get_complexity_expression(rhs_new)
+            reward = obs_old_complexity - obs_new_complexity
+
+        if self.normalize_rewards:
+            max_reward, min_reward = self.reward_solved, self.reward_invalid_equation
+            reward = 2 * (reward - min_reward) / (max_reward - min_reward) - 1
+        return reward
+
+    def get_valid_action_mask(self):
+        return self.action_mask
+
+    def current_state(self):
+        raw_obs = self.obs.astype(np.float32)
+        return raw_obs, raw_obs
+
+
+    def get_done_winner(self):
+        """
+        Returns:
+            A tuple (done, winner) where:
+            - done (bool): True if the episode is over (e.g., solved, invalid, or max steps reached).
+            - winner (int): In single-agent tasks, you can return 0 if solved, or -1 otherwise.
+        """
+        is_solved = check_eqn_solved(self.lhs, self.rhs, self.main_eqn)
+        if is_solved:
+            return True, 0
+        else:
+            return False, -1
+
+    @property
+    def current_player(self):
+        return 0
+
+
+    @property
+    def legal_actions(self):
+        # Return indices where the action mask is True.
+        return [i for i, valid in enumerate(self.action_mask) if valid]
+
+
+    def close(self):
+        """
+        Clean up resources.
+        """
+        self._init_flag = False
+
+    def seed(self, seed: int = 0, dynamic_seed: bool = True):
+        """
+        Set the random seed for the environment.
+        """
+        np.random.seed(seed)
+
+    def __repr__(self):
+        """
+        Return a string representation of the environment.
+        """
+        return f"singleEqn(main_eqn={self.main_eqn}"
+
+    @property
+    def observation_space(self):
+        """
+        Return the observation space.
+        """
+        return self._observation_space
+
+    @property
+    def action_space(self):
+        """
+        Return the action space.
+        """
+        return self._action_space
+
+    @property
+    def reward_space(self):
+        """
+        Return the reward space.
+        """
+        return self._reward_space
diff --git a/zoo/custom_envs/equation_solver/environment_description.md b/zoo/custom_envs/equation_solver/environment_description.md
new file mode 100644
index 000000000..bbce35317
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/environment_description.md
@@ -0,0 +1,57 @@
+# Introduction
+
+Here we define the Markov Decision Process for our environment.
+
+# Markov Decision Process
+
+![Expression Trees](tree.png)
+
+**States**: Equations like $ax+b = 0$ or $cx+d = -x/b$. We vectorize these using a given equation's expression tree, a representation in which the operations and terms within the equation are represented by nodes and edges. Figure 1 shows the expression trees for $ax+b$ and $ax^2 + bx + c$ as examples. To extract a vector from a given tree, we use preorder traversal, which produces a list of operations and terms of variable length that we pad to a maximum length $L=50$ (chosen heuristically). A typical vector might look like $[b, \text{plus}, \text{times}, a, x]$. Finally, we use a feature dictionary to map operations and symbols to integers $\{\text{add}:1, \text{subtract}:2, \text{multiply}:3, \ldots, x:5, a:6, \ldots\}$. For example, the vector representation of $x+3$ would be $[3, 5, 0, 0, 0, 0, 0]$.
+
+At any point in solving the environment, there will be a left-hand side (*lhs*) and a right-hand side (*rhs*) (e.g., $ax = -b$). The state for our environment will be a concatenation of these: $state = (f(\text{lhs}), f(\text{rhs}))$, where $f$ denotes the featurization mentioned above. When we employ a GNN architecture, we also provide the adjacency matrix (derived from the tree) to the network.
+
+**Actions**: Represented as $(operation, term)$ pairs, such as $(\text{sub}, b)$ or $(\text{div}, a)$. How do we choose which operations and terms to include? For the operations, a simple choice is the arithmetic operations $O = (\text{add}, \text{sub}, \text{mul}, \text{div})$. For terms, one could choose the variables that occur in the equation. If this equation were $ax+b=0$, say, the term set would be $T = (x, a, b)$. The action set in turn would be $A = O \times T = \{(\text{add}, x), (\text{sub}, x), \dots, (\text{div}, b)\}$.
+
+This action formulation is sufficient to solve very simple equations like $ax+b=0$ (the solution actions are $(\text{sub}, b), (\text{div}, a)$, which are in $T$), but not big enough to solve more complex equations like $(ax+b)/(cx+d)+e=0$. You can see that after subtracting $e$ from both sides of this rational equation, the next step is to multiply by the divisor $(cx+d)$—but this $(cx+d)$ is not part of the term set that just contains the variables $T = (a, b, c, d, e)$.
+
+To overcome this limitation, we expand the term set by including all *subexpressions* that appear in the equation. The sub-expressions for $ax+b$ and $(ax+b)/(cx+d)+e=0$ are given below:  
+- $ax+b=0 \Rightarrow \{a, x, ax, b\}$  
+- $(ax+b)/(cx+d)+e=0 \Rightarrow \{a, x, ax, b, c, d, cx, cx+d, ax+b\}$
+
+This term set is expressive enough to solve the rational equation and all equations we consider in this paper. Importantly, it is also *dynamic*; the list of subexpressions is derived from the equation/state and thus has variable length.
+
+Looping back to the set of operations, we also add some of SymPy's internal functions:  
+- $\text{expand}: (x + 1)^2 \rightarrow x^2 + 2x + 1$  
+- $\text{factor}: x^2 - 1 \rightarrow (x - 1)(x + 1)$  
+- $\text{collect}: a \cdot x + b \cdot x \rightarrow (a + b) \cdot x$  
+- $\text{square}: x \rightarrow x^2$  
+- $\text{square root}: x^2 \rightarrow x$
+
+Our reasoning here is that these higher-level operations provide structured transformations that simplify equation solving and reduce the number of required steps. Including these operations allows the agent to generalize beyond simple linear equations and efficiently handle more complex algebraic structures, such as rational and polynomial equations, without requiring a massive number of atomic arithmetic operations.
+
+Most of these additional operations do not require an input, so we sub $\text{None}$ into the $(operation, term)$ tuple. The exception is $\text{collect}$, which requires a term. We allow collection only on the variable $x$. We also add an action $(\text{multiply}, -1)$ so that equations of the form $-ax=b$ can be solved. The agent can recreate $(\text{divide}, -a)$ by $(\text{multiply}, -1)$ followed by $(\text{divide}, a)$.
+
+Our final action set is thus:  
+- $O_1 = \{\text{add}, \text{subtract}, \text{multiply}, \text{divide}\}$  
+- $O_2 = \{\text{expand}, \text{factor}, \text{square}, \text{square root}\}$  
+- $\text{Terms} \; T = \{\text{sub-expressions of } \text{lhs}\} \cup \{\text{sub-expressions of } \text{rhs}\}$  
+- $\text{Actions} \; A = (O_1 \times T) \cup (O_2 \times \{\text{None}\}) \cup \{(\text{collect}, x)\} \cup \{(\text{multiply}, -1)\}$
+
+The action set $A$ is indexed serially, mapping an integer $i$ selected by the agent to an $(operation, term)$ pair. As $A$’s size varies with the equation state, we cap it at $|A|=50$ and mask illegal actions (e.g., division by zero).
+
+**Rewards**: We encourage the RL agent to simplify the equation by assigning a complexity score $C$ to each equation, defined as the total number of nodes plus edges in the expression tree. For the examples in Figure 1, this would be:  
+- $C(ax+b) = N_{\text{nodes}} + N_{\text{edges}} = 5 + 4 = 9$  
+- $C(ax^2 + bx + c) = N_{\text{nodes}} + N_{\text{edges}} = 10 + 9 = 19$
+
+The reward is defined as the reduction in the complexity of the equation:  
+- $R(\text{action}) = C(\text{equation}) - C(\text{equation after action})$
+
+The intuition here is to encourage the agent to take actions that simplify equations.
+
+**State Transition Function**: We set up our RL problem using Python and use the SymPy package to represent the equation state $ax+b$. It handles the algebraic manipulation required to transition between states. At each step, we keep track of an $\text{lhs} = (ax+b)$ and $\text{rhs} = 0$. We apply the action to both the lhs and rhs. For example, $(\text{subtract}, b)$ results in $(\text{lhs}, \text{rhs}) = (ax, -b)$, and then $(\text{divide}, a)$ results in $(\text{lhs}, \text{rhs}) = (x, -b/a)$. The terminal condition for the environment is when $\text{lhs}=x$ and the $\text{rhs} = -b/a$ when substituted into the original equation $ax+b$ simplifies to $0$. There were some illegal actions, such as division by zero, we needed to prohibit (see Appendix).
+
+**Limitations**: Importantly, this MDP formulation only works on equations that are "closed," in the sense that solving them requires manipulating the terms already present in the equation/in the sub-expression list. By contrast, solving "open" equations requires adding new, out-of-equation terms or clever substitutions. A classic example is the quadratic equation $ax^2 + bx + c = 0$. This can be solved two ways. First, you subtract $c$ from both sides to generate $(\text{lhs}, \text{rhs}) = (ax^2 + bx, -c)$. The next move is to complete the square by adding $(b/2a)^2$ to each side—an instance of "generative" reasoning, since the term $(b/2a)^2$ is *not* in the term set/list of sub-expressions we have defined.
+
+The second way to solve the quadratic is via a change of variables $y \rightarrow x - b/(2a)$, which, after some algebraic simplifications, reduces the equation to $a y^2 + c - b^2/(4a)$, which is now "closed" and can be solved with the action sequence $(\text{sub}, c - b^2/(4a)), (\text{div}, a), (\text{sqrt}, \text{None})$.
+
+Equations that require these more exotic actions (change of variable and completing the square by adding out-of-equation terms) are beyond the scope of the current work.
\ No newline at end of file
diff --git a/zoo/custom_envs/equation_solver/my_alphazero_mlp_model.py b/zoo/custom_envs/equation_solver/my_alphazero_mlp_model.py
new file mode 100644
index 000000000..a86d5ec0d
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/my_alphazero_mlp_model.py
@@ -0,0 +1,119 @@
+"""
+A custom MLP-based AlphaZero model for vector inputs.
+This model is designed for tasks where the state is a simple vector (e.g., [3, -2, 1, 10, ...]).
+We support two observation shape formats:
+  - A flat vector, e.g. (41,)
+  - A pseudo-image, e.g. (1, 41, 1)
+In either case, the input is flattened and passed through an MLP to produce both policy logits and a value.
+"""
+
+from typing import Tuple, Optional, Sequence
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from ding.torch_utils import MLP  # DI-engine's MLP utility
+from ding.utils import MODEL_REGISTRY, SequenceType
+
+@MODEL_REGISTRY.register('AlphaZeroMLPModel')
+class AlphaZeroMLPModel(nn.Module):
+    def __init__(
+        self,
+        observation_shape: SequenceType = (41,), 
+        action_space_size: int = 50,
+        categorical_distribution: bool = False,
+        activation: Optional[nn.Module] = nn.ReLU(inplace=True),
+        hidden_sizes: SequenceType = [128, 128],
+        last_linear_layer_init_zero: bool = True,
+    ):
+        """
+        Args:
+            observation_shape: Expected shape of observations.
+                Can be a flat vector, e.g. (41,), or a pseudo-image, e.g. (1, 41, 1).
+            action_space_size: Number of discrete actions.
+            categorical_distribution: If True, use a categorical representation for value.
+            activation: Activation function.
+            hidden_sizes: List of hidden layer sizes for the shared MLP.
+            last_linear_layer_init_zero: Whether to initialize the last layer of the heads with zeros.
+        """
+        super(AlphaZeroMLPModel, self).__init__()
+        self.categorical_distribution = categorical_distribution
+        self.observation_shape = observation_shape
+        self.value_support_size = 601 if self.categorical_distribution else 1
+        self.last_linear_layer_init_zero = last_linear_layer_init_zero
+
+        # Determine the input dimension based on the observation shape.
+        if len(observation_shape) == 1:
+            self.input_dim = observation_shape[0]
+        elif len(observation_shape) == 3:
+            self.input_dim = observation_shape[0] * observation_shape[1] * observation_shape[2]
+        else:
+            raise ValueError(f"Unsupported observation_shape: {observation_shape}")
+
+        self.action_space_size = action_space_size
+
+        # Shared representation network: a simple MLP.
+        self.representation_network = MLP(
+            in_channels=self.input_dim,
+            hidden_channels=hidden_sizes[0],
+            out_channels=hidden_sizes[-1],
+            layer_num=len(hidden_sizes),
+            activation=activation,
+        )
+
+        # Policy head: maps shared representation to action logits.
+        self.policy_head = MLP(
+            in_channels=hidden_sizes[-1],
+            hidden_channels=hidden_sizes[0],
+            out_channels=action_space_size,
+            layer_num=len(hidden_sizes),
+            activation=activation,
+            last_linear_layer_init_zero=last_linear_layer_init_zero,
+        )
+
+        # Value head: maps shared representation to a scalar value.
+        self.value_head = MLP(
+            in_channels=hidden_sizes[-1],
+            hidden_channels=hidden_sizes[0],
+            out_channels=self.value_support_size,
+            layer_num=len(hidden_sizes),
+            activation=activation,
+            last_linear_layer_init_zero=last_linear_layer_init_zero,
+        )
+
+    def forward(self, state_batch: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+            state_batch: Tensor of shape (B, C, H, W) or (B, L) if flat.
+        Returns:
+            Tuple (policy_logits, value) where:
+              - policy_logits: (B, action_space_size)
+              - value: (B, 1) (if not using categorical distribution)
+        """
+        B = state_batch.size(0)
+        # If the state is 4D, flatten; if it's 2D, it's already flat.
+        if state_batch.dim() == 4:
+            x = state_batch.view(B, -1)
+        else:
+            x = state_batch
+        rep = self.representation_network(x)
+        policy_logits = self.policy_head(rep)
+        value = self.value_head(rep)
+        if not self.categorical_distribution:
+            value = value.unsqueeze(-1)
+        return policy_logits, value
+
+    def compute_policy_value(self, state_batch: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Computes the softmax probabilities and state value.
+        """
+        logits, value = self.forward(state_batch)
+        prob = F.softmax(logits, dim=-1)
+        return prob, value
+
+    def compute_logp_value(self, state_batch: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Computes the log-softmax probabilities and state value.
+        """
+        logits, value = self.forward(state_batch)
+        log_prob = F.log_softmax(logits, dim=-1)
+        return log_prob, value
diff --git a/zoo/custom_envs/equation_solver/my_alphazero_policy.py b/zoo/custom_envs/equation_solver/my_alphazero_policy.py
new file mode 100644
index 000000000..e0390cb8c
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/my_alphazero_policy.py
@@ -0,0 +1,19 @@
+from lzero.policy.alphazero import AlphaZeroPolicy
+from ding.policy.base_policy import POLICY_REGISTRY
+from ding.utils import ENV_REGISTRY
+from easydict import EasyDict
+
+@POLICY_REGISTRY.register('MyAlphaZeroPolicy')
+class MyAlphaZeroPolicy(AlphaZeroPolicy):
+    def _get_simulation_env(self):
+        # Create a simulation environment using the merged environment config.
+        sim_cfg = EasyDict(self.cfg.get('env', {}))
+        sim_env = ENV_REGISTRY.get(self.cfg.simulation_env_id)(cfg=sim_cfg)
+        return sim_env
+
+    @property
+    def simulate_env(self):
+        # Lazily initialize and cache the simulation environment.
+        if not hasattr(self, '_simulate_env'):
+            self._simulate_env = self._get_simulation_env()
+        return self._simulate_env
diff --git a/zoo/custom_envs/equation_solver/my_train_alphazero.py b/zoo/custom_envs/equation_solver/my_train_alphazero.py
new file mode 100644
index 000000000..b7e86f8f6
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/my_train_alphazero.py
@@ -0,0 +1,150 @@
+import logging
+import os
+from functools import partial
+from typing import Optional, Tuple
+
+import torch
+from ding.config import compile_config
+from ding.envs import create_env_manager
+from ding.envs import get_vec_env_setting
+from ding.policy import create_policy
+from ding.utils import set_pkg_seed
+from ding.worker import BaseLearner, create_buffer
+from tensorboardX import SummaryWriter
+
+from lzero.policy import visit_count_temperature
+from lzero.worker import AlphaZeroCollector, AlphaZeroEvaluator
+
+
+def my_train_alphazero(
+        input_cfg: Tuple[dict, dict],
+        seed: int = 0,
+        model: Optional[torch.nn.Module] = None,
+        model_path: Optional[str] = None,
+        max_train_iter: Optional[int] = int(1e10),
+        max_env_step: Optional[int] = int(1e10),
+) -> 'Policy':  # noqa
+    """
+    Overview:
+        The train entry for AlphaZero.
+    Arguments:
+        - input_cfg (:obj:`Tuple[dict, dict]`): Config in dict type.
+            ``Tuple[dict, dict]`` type means [user_config, create_cfg].
+        - seed (:obj:`int`): Random seed.
+        - env_setting (:obj:`Optional[List[Any]]`): A list with 3 elements: \
+            ``BaseEnv`` subclass, collector env config, and evaluator env config.
+        - model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module.
+        - model_path (:obj:`Optional[str]`): The pretrained model path, which should
+            point to the ckpt file of the pretrained model, and an absolute path is recommended.
+            In LightZero, the path is usually something like ``exp_name/ckpt/ckpt_best.pth.tar``.
+        - max_train_iter (:obj:`Optional[int]`): Maximum policy update iterations in training.
+        - max_env_step (:obj:`Optional[int]`): Maximum collected environment interaction steps.
+    Returns:
+        - policy (:obj:`Policy`): Converged policy.
+    """
+    cfg, create_cfg = input_cfg
+    create_cfg.policy.type = create_cfg.policy.type
+
+    if cfg.policy.cuda and torch.cuda.is_available():
+        cfg.policy.device = 'cuda'
+    else:
+        cfg.policy.device = 'cpu'
+
+    cfg = compile_config(cfg, seed=seed, env=None, auto=True, create_cfg=create_cfg, save_cfg=True)
+
+    # New code: SAFELY REMOVE unwanted keys
+    for key in ["num_res_blocks", "num_channels"]:
+        if key in cfg.policy.model:
+            del cfg.policy.model[key]
+
+    # Create main components: env, policy
+    env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env)
+    collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg])
+    evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg])
+    collector_env.seed(cfg.seed)
+    evaluator_env.seed(cfg.seed, dynamic_seed=False)
+    set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda)
+    policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval'])
+
+    # load pretrained model
+    if model_path is not None:
+        policy.learn_mode.load_state_dict(torch.load(model_path, map_location=cfg.policy.device))
+
+    # Create worker components: learner, collector, evaluator, replay buffer, commander.
+    tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
+    learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
+    replay_buffer = create_buffer(cfg.policy.other.replay_buffer, tb_logger=tb_logger, exp_name=cfg.exp_name)
+
+    policy_config = cfg.policy
+    batch_size = policy_config.batch_size
+    collector = AlphaZeroCollector(
+        env=collector_env,
+        policy=policy.collect_mode,
+        tb_logger=tb_logger,
+        exp_name=cfg.exp_name,
+    )
+    evaluator = AlphaZeroEvaluator(
+        eval_freq=cfg.policy.eval_freq,
+        n_evaluator_episode=cfg.env.n_evaluator_episode,
+        stop_value=cfg.env.stop_value,
+        env=evaluator_env,
+        policy=policy.eval_mode,
+        tb_logger=tb_logger,
+        exp_name=cfg.exp_name,
+    )
+
+    # ==============================================================
+    # Main loop
+    # ==============================================================
+    # Learner's before_run hook.
+    learner.call_hook('before_run')
+    if cfg.policy.update_per_collect is not None:
+        update_per_collect = cfg.policy.update_per_collect
+    while True:
+        collect_kwargs = {}
+        # set temperature for visit count distributions according to the train_iter,
+        # please refer to Appendix D in MuZero paper for details.
+        collect_kwargs['temperature'] = visit_count_temperature(
+            policy_config.manual_temperature_decay,
+            policy_config.fixed_temperature_value,
+            policy_config.threshold_training_steps_for_final_temperature,
+            trained_steps=learner.train_iter
+        )
+
+        # Evaluate policy performance
+        if evaluator.should_eval(learner.train_iter) and learner.train_iter > 0:
+            stop, reward = evaluator.eval(
+                learner.save_checkpoint,
+                learner.train_iter,
+                collector.envstep,
+            )
+            if stop:
+                break
+
+        # Collect data by default config n_sample/n_episode
+        new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs)
+        new_data = sum(new_data, [])
+        if cfg.policy.update_per_collect is None:
+            # update_per_collect is None, then update_per_collect is set to the number of collected transitions multiplied by the replay_ratio.
+            collected_transitions_num = len(new_data)
+            update_per_collect = int(collected_transitions_num * cfg.policy.replay_ratio)
+        replay_buffer.push(new_data, cur_collector_envstep=collector.envstep)
+
+        # Learn policy from collected data
+        for i in range(update_per_collect):
+            # Learner will train ``update_per_collect`` times in one iteration.
+            train_data = replay_buffer.sample(batch_size, learner.train_iter)
+            if train_data is None:
+                logging.warning(
+                    'The data in replay_buffer is not sufficient to sample a mini-batch.'
+                    'continue to collect now ....'
+                )
+                break
+
+            learner.train(train_data, collector.envstep)
+        if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter:
+            break
+
+    # Learner's after_run hook.
+    learner.call_hook('after_run')
+    return policy
diff --git a/zoo/custom_envs/equation_solver/read_lc_muzero_easy.ipynb b/zoo/custom_envs/equation_solver/read_lc_muzero_easy.ipynb
new file mode 100644
index 000000000..6e5476eeb
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/read_lc_muzero_easy.ipynb
@@ -0,0 +1,789 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "### Notes\n",
+    "\n",
+    "1. Might want to increase the MAX_STEP and equation length -- muzero might be able to exploit longer things. \n",
+    "2. Check how large the depth search is\n",
+    "3. Action masking, illegal action.\n",
+    "4. Debug the env... there is some shenanigans going on... aren't illegal equations supposedly impossible?\n",
+    "5. Then curriculum learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [0.24208333333333332, 0.3475, 0.34312499999999996, 0.21624999999999997, 0.41687499999999994, 0.41374999999999995, 0.93375, 0.808125, 1.0, 1.0, 1.0, 1.0, 0.5475, 0.86375, 1.0, -0.040000000000000036, 0.026249999999999968, 0.41625, -0.040000000000000036, 0.29]\n",
+      "Min Rewards: [-0.1100000000000001, -0.08000000000000007, -0.10000000000000009, -0.1100000000000001, -0.09000000000000008, -0.10000000000000009, -0.06000000000000005, -0.040000000000000036, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, -0.09000000000000008, 1.0, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.06000000000000005]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, 1.0, 1.0, -0.040000000000000036, 1.0]\n",
+      "Timesteps: [176, 220, 258, 298, 334, 372, 390, 412, 428, 448, 464, 482, 518, 542, 558, 606, 652, 686, 734, 776]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 176 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Mean Rewards: [0.24208333333333332, 0.3475, 0.34312499999999996, 0.21624999999999997, 0.41687499999999994, 0.41374999999999995, 0.93375, 0.808125, 1.0, 1.0, 1.0, 1.0, 0.5475, 0.86375, 1.0, -0.040000000000000036, 0.026249999999999968, 0.41625, -0.040000000000000036, 0.29, -0.03625000000000003, 0.351875, 0.09624999999999997, 0.35124999999999995, 0.48, 0.024999999999999967, -0.040000000000000036, -0.038750000000000034, 0.41625, 0.08499999999999996, 0.14812499999999995, 0.15562499999999996, -0.040000000000000036]\n",
+      "Training Min Rewards: [-0.1100000000000001, -0.08000000000000007, -0.10000000000000009, -0.1100000000000001, -0.09000000000000008, -0.10000000000000009, -0.06000000000000005, -0.040000000000000036, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, -0.09000000000000008, 1.0, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.06000000000000005, -0.06000000000000005, -0.08000000000000007, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.08000000000000007, -0.08000000000000007, -0.1100000000000001, -0.040000000000000036]\n",
+      "Training Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, 1.0, 1.0, -0.040000000000000036, 1.0, -0.030000000000000027, 1.0, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, -0.020000000000000018, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036]\n",
+      "Training Timesteps: [176, 220, 258, 298, 334, 372, 390, 412, 428, 448, 464, 482, 518, 542, 558, 606, 652, 686, 734, 776, 824, 864, 908, 948, 984, 1030, 1078, 1126, 1160, 1204, 1246, 1288, 1336]\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Evaluation Mean Rewards: []\n",
+      "Evaluation Min Rewards: []\n",
+      "Evaluation Max Rewards: []\n",
+      "Evaluation Episode Return Means: []\n",
+      "Evaluation Timesteps: []\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 1 at timestep: 176 with reward: 1.0\n",
+      "The evaluation lists are empty. Please check your evaluation data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Paths to experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "evaluator_log_dir = os.path.join(data_dir, \"log\", \"evaluator\")\n",
+    "\n",
+    "# Initialize storage for reward statistics\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug: Print parsed training data\n",
+    "print(\"Training Mean Rewards:\", mean_rewards)\n",
+    "print(\"Training Min Rewards:\", min_rewards)\n",
+    "print(\"Training Max Rewards:\", max_rewards)\n",
+    "print(\"Training Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# ------------------------- EVALUATION LOG READING -------------------------\n",
+    "\n",
+    "# Initialize storage for evaluation statistics\n",
+    "eval_mean_rewards = []\n",
+    "eval_min_rewards = []\n",
+    "eval_max_rewards = []\n",
+    "eval_episode_return_means = []\n",
+    "eval_timesteps = []\n",
+    "\n",
+    "# Read the `evaluator_logger.txt` file\n",
+    "evaluator_log_file = os.path.join(evaluator_log_dir, \"evaluator_logger.txt\")\n",
+    "if os.path.isfile(evaluator_log_file):\n",
+    "    with open(evaluator_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            # Extract only numerical lines, avoiding column headers\n",
+    "            if \"reward_mean\" in line and \"Name\" not in line:\n",
+    "                parts = line.split(\"|\")\n",
+    "                try:\n",
+    "                    eval_mean_rewards.append(float(parts[-4].strip()))\n",
+    "                    eval_min_rewards.append(float(parts[-1].strip()))\n",
+    "                    eval_max_rewards.append(float(parts[-2].strip()))\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid line: {line.strip()}\")\n",
+    "            elif \"eval_episode_return_mean\" in line:\n",
+    "                try:\n",
+    "                    eval_episode_return_means.append(float(line.split(\"|\")[-1].strip()))\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid eval_episode_return_mean line: {line.strip()}\")\n",
+    "            elif \"envstep_count\" in line and \"ckpt_name\" not in line:\n",
+    "                try:\n",
+    "                    eval_timesteps.append(int(float(line.split(\"|\")[-1].strip())))  # Convert to integer\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid envstep_count line: {line.strip()}\")\n",
+    "else:\n",
+    "    print(f\"No evaluator log file found at {evaluator_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug: Print parsed evaluation data\n",
+    "print(\"Evaluation Mean Rewards:\", eval_mean_rewards)\n",
+    "print(\"Evaluation Min Rewards:\", eval_min_rewards)\n",
+    "print(\"Evaluation Max Rewards:\", eval_max_rewards)\n",
+    "print(\"Evaluation Episode Return Means:\", eval_episode_return_means)\n",
+    "print(\"Evaluation Timesteps:\", eval_timesteps)\n",
+    "\n",
+    "# ------------------------- PLOTTING -------------------------\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Plot training rewards\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards, fmt=\"-o\", capsize=5, label=\"Training Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Training Reward Range (Min-Max)\"\n",
+    ")\n",
+    "\n",
+    "# Plot evaluation rewards\n",
+    "if eval_timesteps:\n",
+    "    plt.errorbar(\n",
+    "        eval_timesteps, eval_mean_rewards, fmt=\"-s\", capsize=5, label=\"Eval Reward Mean\", color=\"red\"\n",
+    "    )\n",
+    "    plt.fill_between(\n",
+    "        eval_timesteps, eval_min_rewards, eval_max_rewards, color=\"red\", alpha=0.2, label=\"Eval Reward Range (Min-Max)\"\n",
+    "    )\n",
+    "\n",
+    "plt.xlabel(\"Timesteps\")\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Training & Evaluation Rewards for Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# ------------------------- FIRST SUCCESSFUL TIMESTEP -------------------------\n",
+    "\n",
+    "# Ensure the lists are not empty before checking for max rewards\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 1 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your training data.\")\n",
+    "\n",
+    "if eval_max_rewards and eval_timesteps:\n",
+    "    for i, reward in enumerate(eval_max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First evaluation max reward > 1 at timestep: {eval_timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The evaluation lists are empty. Please check your evaluation data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Mean Rewards: [0.25203125, 0.21687499999999998, 0.14624999999999994, 0.35062499999999996, 0.15249999999999997, 0.33999999999999997, 0.14874999999999997, 0.27749999999999997, 0.15312499999999996, 0.21062499999999995, 0.15124999999999997, 0.40875, 0.34375, 0.341875, 0.08312499999999996, 0.27375, 0.346875, -0.04187500000000004, 0.33999999999999997, 0.48374999999999996, 0.345625, 0.41812499999999997, 0.34562499999999996, 0.07999999999999996, 0.21687499999999998, 0.15999999999999998, 0.14687499999999995, 0.07187499999999995, 0.27124999999999994, 0.14999999999999997, 0.345625, 0.34624999999999995, 0.21374999999999994, 0.464375, 0.41562499999999997, 0.21187499999999998, 0.34062499999999996, 0.021249999999999963, 0.08499999999999996, 0.4225, 0.21249999999999997, 0.281875, 0.274375, 0.40875, 0.07624999999999996, 0.07749999999999996, 0.2875, 0.20437499999999995, 0.28625, 0.21062499999999995, 0.20124999999999998, 0.41312499999999996, 0.279375, 0.21562499999999998, 0.21687499999999998, 0.354375, -0.05500000000000005, 0.345, 0.01812499999999996, 0.416875, 0.21749999999999997, 0.21437499999999998, 0.344375, 0.33999999999999997, 0.28812499999999996, 0.004999999999999949, 0.08624999999999997, 0.415, 0.4125, 0.33812499999999995, 0.39562499999999995, 0.27937499999999993, 0.13687499999999994, 0.26562499999999994, 0.15062499999999995, 0.22124999999999997, 0.15125, 0.27374999999999994, 0.019999999999999962, 0.27749999999999997, 0.21124999999999997, 0.07812499999999996, 0.28437499999999993, 0.34624999999999995, 0.27874999999999994, 0.40875, 0.285, 0.14374999999999996, 0.34124999999999994, 0.22249999999999998]\n",
+      "Training Min Rewards: [-0.1100000000000001, -0.10000000000000009, -0.09000000000000008, -0.07000000000000006, -0.1100000000000001, -0.1100000000000001, -0.09000000000000008, -0.1100000000000001, -0.10000000000000009, -0.1100000000000001, -0.08000000000000007, -0.1100000000000001, -0.10000000000000009, -0.1100000000000001, -0.1100000000000001, -0.1100000000000001, -0.1100000000000001, -0.08000000000000007, -0.09000000000000008, -0.06000000000000005, -0.08000000000000007, -0.10000000000000009, -0.1100000000000001, -0.10000000000000009, -0.08000000000000007, -0.06000000000000005, -0.10000000000000009, -0.10000000000000009, -0.1100000000000001, -0.1100000000000001, -0.10000000000000009, -0.1100000000000001, -0.1100000000000001, -0.1100000000000001, -0.07000000000000006, -0.08000000000000007, -0.10000000000000009, -0.10000000000000009, -0.10000000000000009, -0.08000000000000007, -0.1100000000000001, -0.1100000000000001, -0.10000000000000009, -0.08000000000000007, -0.1100000000000001, -0.1100000000000001, -0.08000000000000007, -0.1100000000000001, -0.08000000000000007, -0.08000000000000007, -0.08000000000000007, -0.1100000000000001, -0.08000000000000007, -0.1100000000000001, -0.09000000000000008, -0.08000000000000007, -0.1100000000000001, -0.1100000000000001, -0.08000000000000007, -0.050000000000000044, -0.08000000000000007, -0.1100000000000001, -0.09000000000000008, -0.1100000000000001, -0.08000000000000007, -0.1100000000000001, -0.1100000000000001, -0.08000000000000007, -0.1100000000000001, -0.10000000000000009, -0.1100000000000001, -0.1100000000000001, -0.1100000000000001, -0.1100000000000001, -0.09000000000000008, -0.07000000000000006, -0.1100000000000001, -0.1100000000000001, -0.08000000000000007, -0.10000000000000009, -0.1100000000000001, -0.10000000000000009, -0.09000000000000008, -0.10000000000000009, -0.10000000000000009, -0.1100000000000001, -0.1100000000000001, -0.1100000000000001, -0.08000000000000007, -0.07000000000000006]\n",
+      "Training Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
+      "Training Timesteps: [170, 210, 254, 294, 336, 374, 416, 458, 500, 544, 588, 624, 660, 698, 742, 784, 822, 870, 910, 942, 980, 1016, 1056, 1100, 1142, 1184, 1226, 1270, 1310, 1352, 1392, 1432, 1474, 1510, 1548, 1588, 1628, 1674, 1718, 1754, 1794, 1836, 1878, 1912, 1956, 2000, 2040, 2084, 2124, 2164, 2204, 2244, 2282, 2324, 2366, 2404, 2452, 2488, 2534, 2568, 2610, 2650, 2688, 2726, 2766, 2812, 2858, 2900, 2938, 2974, 3012, 3054, 3096, 3134, 3178, 3220, 3266, 3310, 3356, 3396, 3436, 3480, 3518, 3556, 3596, 3632, 3672, 3714, 3752, 3796]\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                  | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                | eval_episode_return_mean |\n",
+      "Evaluation Mean Rewards: []\n",
+      "Evaluation Min Rewards: []\n",
+      "Evaluation Max Rewards: []\n",
+      "Evaluation Episode Return Means: []\n",
+      "Evaluation Timesteps: []\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 1 at timestep: 170 with reward: 1.0\n",
+      "The evaluation lists are empty. Please check your evaluation data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Paths to experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "evaluator_log_dir = os.path.join(data_dir, \"log\", \"evaluator\")\n",
+    "\n",
+    "# Initialize storage for reward statistics\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug: Print parsed training data\n",
+    "print(\"Training Mean Rewards:\", mean_rewards)\n",
+    "print(\"Training Min Rewards:\", min_rewards)\n",
+    "print(\"Training Max Rewards:\", max_rewards)\n",
+    "print(\"Training Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# ------------------------- EVALUATION LOG READING -------------------------\n",
+    "\n",
+    "# Initialize storage for evaluation statistics\n",
+    "eval_mean_rewards = []\n",
+    "eval_min_rewards = []\n",
+    "eval_max_rewards = []\n",
+    "eval_episode_return_means = []\n",
+    "eval_timesteps = []\n",
+    "\n",
+    "# Read the `evaluator_logger.txt` file\n",
+    "evaluator_log_file = os.path.join(evaluator_log_dir, \"evaluator_logger.txt\")\n",
+    "if os.path.isfile(evaluator_log_file):\n",
+    "    with open(evaluator_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            # Extract only numerical lines, avoiding column headers\n",
+    "            if \"reward_mean\" in line and \"Name\" not in line:\n",
+    "                parts = line.split(\"|\")\n",
+    "                try:\n",
+    "                    eval_mean_rewards.append(float(parts[-4].strip()))\n",
+    "                    eval_min_rewards.append(float(parts[-1].strip()))\n",
+    "                    eval_max_rewards.append(float(parts[-2].strip()))\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid line: {line.strip()}\")\n",
+    "            elif \"eval_episode_return_mean\" in line:\n",
+    "                try:\n",
+    "                    eval_episode_return_means.append(float(line.split(\"|\")[-1].strip()))\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid eval_episode_return_mean line: {line.strip()}\")\n",
+    "            elif \"envstep_count\" in line and \"ckpt_name\" not in line:\n",
+    "                try:\n",
+    "                    eval_timesteps.append(int(float(line.split(\"|\")[-1].strip())))  # Convert to integer\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid envstep_count line: {line.strip()}\")\n",
+    "else:\n",
+    "    print(f\"No evaluator log file found at {evaluator_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug: Print parsed evaluation data\n",
+    "print(\"Evaluation Mean Rewards:\", eval_mean_rewards)\n",
+    "print(\"Evaluation Min Rewards:\", eval_min_rewards)\n",
+    "print(\"Evaluation Max Rewards:\", eval_max_rewards)\n",
+    "print(\"Evaluation Episode Return Means:\", eval_episode_return_means)\n",
+    "print(\"Evaluation Timesteps:\", eval_timesteps)\n",
+    "\n",
+    "# ------------------------- PLOTTING -------------------------\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Plot training rewards\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards, fmt=\"-o\", capsize=5, label=\"Training Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Training Reward Range (Min-Max)\"\n",
+    ")\n",
+    "\n",
+    "# Plot evaluation rewards\n",
+    "if eval_timesteps:\n",
+    "    plt.errorbar(\n",
+    "        eval_timesteps, eval_mean_rewards, fmt=\"-s\", capsize=5, label=\"Eval Reward Mean\", color=\"red\"\n",
+    "    )\n",
+    "    plt.fill_between(\n",
+    "        eval_timesteps, eval_min_rewards, eval_max_rewards, color=\"red\", alpha=0.2, label=\"Eval Reward Range (Min-Max)\"\n",
+    "    )\n",
+    "\n",
+    "plt.xlabel(\"Timesteps\")\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Training & Evaluation Rewards for Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# ------------------------- FIRST SUCCESSFUL TIMESTEP -------------------------\n",
+    "\n",
+    "# Ensure the lists are not empty before checking for max rewards\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 1 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your training data.\")\n",
+    "\n",
+    "if eval_max_rewards and eval_timesteps:\n",
+    "    for i, reward in enumerate(eval_max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First evaluation max reward > 1 at timestep: {eval_timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The evaluation lists are empty. Please check your evaluation data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Mean Rewards: [0.31328125, 0.35, 0.3475, 0.6125, 0.345, 0.36250000000000004, 0.08499999999999996, 0.34875, 0.3475, 0.48375, 0.21249999999999997, 0.35, 0.46499999999999997, 0.33875, 0.22374999999999998, 0.07749999999999996, -0.04875000000000004, 0.34124999999999994, 0.355, 0.3424999999999999, 0.20124999999999996, 0.20874999999999996, 0.21249999999999997, 0.23249999999999998, 0.08999999999999997, 0.21374999999999997, 0.08749999999999997, 0.19624999999999995, 0.20499999999999996, 0.07499999999999996, 0.08749999999999997, 0.22374999999999998, 0.48, 0.35124999999999995, 0.22374999999999998, -0.07125000000000006, 0.33624999999999994, -0.04875000000000004, 0.07874999999999996, 0.07874999999999996, 0.21874999999999997, 0.4825, -0.051250000000000046, 0.34875, 0.3425, 0.35874999999999996, -0.04625000000000004, 0.34249999999999997, 0.48624999999999996, 0.07749999999999996, 0.09499999999999997, 0.22124999999999997, 0.08499999999999996, 0.09124999999999997, 0.05499999999999994, 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+      "Training Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, 1.0, -0.020000000000000018, 1.0, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.030000000000000027, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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+      "Training Timesteps: [152, 170, 188, 204, 224, 242, 264, 284, 304, 320, 342, 362, 382, 400, 422, 444, 468, 486, 506, 524, 544, 564, 586, 608, 630, 652, 674, 696, 718, 740, 762, 784, 800, 818, 842, 866, 884, 908, 930, 952, 972, 990, 1014, 1032, 1050, 1068, 1092, 1112, 1128, 1150, 1172, 1194, 1216, 1238, 1260, 1278, 1298, 1318, 1334, 1356, 1374, 1398, 1414, 1430, 1450, 1468, 1488, 1506, 1524, 1542, 1564, 1584, 1604, 1626, 1648, 1672, 1692, 1708, 1728, 1748, 1772, 1792, 1810, 1832, 1854, 1872, 1894, 1910, 1926, 1950, 1974, 1992, 2010, 2030, 2050, 2074, 2094, 2114, 2132, 2154, 2170, 2190, 2208, 2222, 2240, 2264, 2282, 2300, 2324, 2346, 2366, 2384, 2404, 2424, 2446, 2464, 2484, 2504, 2526, 2546, 2560, 2582, 2606, 2628, 2646, 2664, 2682, 2700, 2720, 2742, 2758, 2780, 2802, 2822, 2844, 2862, 2882, 2906, 2926, 2948, 2970, 2984, 3002, 3024, 3044, 3064, 3086, 3108, 3128, 3148, 3168, 3186, 3206, 3228, 3240, 3258, 3274, 3296, 3316, 3336, 3356, 3376, 3398, 3416, 3434, 3454, 3474, 3492, 3514, 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18832, 18854, 18876, 18894, 18918, 18940, 18958, 18976, 18998, 19014, 19032, 19050, 19072, 19094, 19114, 19136, 19156, 19178, 19196, 19218, 19238, 19260, 19274, 19296, 19316, 19334, 19356, 19376, 19396, 19418, 19440, 19458, 19478, 19496, 19516, 19532, 19554, 19574, 19592, 19614, 19630, 19650, 19672, 19694, 19712, 19730, 19750, 19766, 19788, 19806, 19826, 19846, 19860, 19882, 19906, 19926, 19944, 19962, 19986, 20008, 20030, 20046, 20064, 20080, 20098, 20118, 20140, 20158, 20180, 20200, 20222, 20244, 20264, 20286, 20308, 20328, 20350, 20366, 20388, 20408, 20428, 20450, 20466, 20486, 20506, 20528, 20548, 20560, 20578, 20600, 20624, 20642, 20664, 20682, 20702, 20718, 20740, 20758, 20776, 20796, 20818, 20836, 20856, 20878, 20898, 20920, 20940, 20958, 20980, 21000, 21020, 21042, 21060, 21072, 21094, 21118, 21140, 21158, 21178, 21202, 21220, 21240, 21262, 21278, 21292, 21312, 21330, 21352, 21368, 21390, 21408, 21428, 21450, 21466, 21488, 21508, 21528, 21546, 21568, 21586, 21604, 21626]\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                  | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                  | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                  | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                  | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                  | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                   | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                  | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                               | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                 | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return                                                  | eval_episode_return_mean |\n",
+      "Skipping invalid eval_episode_return_mean line: | Name  | eval_episode_return              | eval_episode_return_mean |\n",
+      "Evaluation Mean Rewards: []\n",
+      "Evaluation Min Rewards: []\n",
+      "Evaluation Max Rewards: []\n",
+      "Evaluation Episode Return Means: []\n",
+      "Evaluation Timesteps: []\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 1 at timestep: 152 with reward: 1.0\n",
+      "The evaluation lists are empty. Please check your evaluation data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Paths to experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "evaluator_log_dir = os.path.join(data_dir, \"log\", \"evaluator\")\n",
+    "\n",
+    "# Initialize storage for reward statistics\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug: Print parsed training data\n",
+    "print(\"Training Mean Rewards:\", mean_rewards)\n",
+    "print(\"Training Min Rewards:\", min_rewards)\n",
+    "print(\"Training Max Rewards:\", max_rewards)\n",
+    "print(\"Training Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# ------------------------- EVALUATION LOG READING -------------------------\n",
+    "\n",
+    "# Initialize storage for evaluation statistics\n",
+    "eval_mean_rewards = []\n",
+    "eval_min_rewards = []\n",
+    "eval_max_rewards = []\n",
+    "eval_episode_return_means = []\n",
+    "eval_timesteps = []\n",
+    "\n",
+    "# Read the `evaluator_logger.txt` file\n",
+    "evaluator_log_file = os.path.join(evaluator_log_dir, \"evaluator_logger.txt\")\n",
+    "if os.path.isfile(evaluator_log_file):\n",
+    "    with open(evaluator_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            # Extract only numerical lines, avoiding column headers\n",
+    "            if \"reward_mean\" in line and \"Name\" not in line:\n",
+    "                parts = line.split(\"|\")\n",
+    "                try:\n",
+    "                    eval_mean_rewards.append(float(parts[-4].strip()))\n",
+    "                    eval_min_rewards.append(float(parts[-1].strip()))\n",
+    "                    eval_max_rewards.append(float(parts[-2].strip()))\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid line: {line.strip()}\")\n",
+    "            elif \"eval_episode_return_mean\" in line:\n",
+    "                try:\n",
+    "                    eval_episode_return_means.append(float(line.split(\"|\")[-1].strip()))\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid eval_episode_return_mean line: {line.strip()}\")\n",
+    "            elif \"envstep_count\" in line and \"ckpt_name\" not in line:\n",
+    "                try:\n",
+    "                    eval_timesteps.append(int(float(line.split(\"|\")[-1].strip())))  # Convert to integer\n",
+    "                except ValueError:\n",
+    "                    print(f\"Skipping invalid envstep_count line: {line.strip()}\")\n",
+    "else:\n",
+    "    print(f\"No evaluator log file found at {evaluator_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug: Print parsed evaluation data\n",
+    "print(\"Evaluation Mean Rewards:\", eval_mean_rewards)\n",
+    "print(\"Evaluation Min Rewards:\", eval_min_rewards)\n",
+    "print(\"Evaluation Max Rewards:\", eval_max_rewards)\n",
+    "print(\"Evaluation Episode Return Means:\", eval_episode_return_means)\n",
+    "print(\"Evaluation Timesteps:\", eval_timesteps)\n",
+    "\n",
+    "# ------------------------- PLOTTING -------------------------\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Plot training rewards\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards, fmt=\"-o\", capsize=5, label=\"Training Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Training Reward Range (Min-Max)\"\n",
+    ")\n",
+    "\n",
+    "# Plot evaluation rewards\n",
+    "if eval_timesteps:\n",
+    "    plt.errorbar(\n",
+    "        eval_timesteps, eval_mean_rewards, fmt=\"-s\", capsize=5, label=\"Eval Reward Mean\", color=\"red\"\n",
+    "    )\n",
+    "    plt.fill_between(\n",
+    "        eval_timesteps, eval_min_rewards, eval_max_rewards, color=\"red\", alpha=0.2, label=\"Eval Reward Range (Min-Max)\"\n",
+    "    )\n",
+    "\n",
+    "plt.xlabel(\"Timesteps\")\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Training & Evaluation Rewards for Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "# ------------------------- FIRST SUCCESSFUL TIMESTEP -------------------------\n",
+    "\n",
+    "# Ensure the lists are not empty before checking for max rewards\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 1 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your training data.\")\n",
+    "\n",
+    "if eval_max_rewards and eval_timesteps:\n",
+    "    for i, reward in enumerate(eval_max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First evaluation max reward > 1 at timestep: {eval_timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The evaluation lists are empty. Please check your evaluation data.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "abel-rl",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zoo/custom_envs/equation_solver/read_learning_curve_alphazero.ipynb b/zoo/custom_envs/equation_solver/read_learning_curve_alphazero.ipynb
new file mode 100644
index 000000000..26883cc95
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/read_learning_curve_alphazero.ipynb
@@ -0,0 +1,593 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "### Notes\n",
+    "\n",
+    "1. Might want to increase the MAX_STEP and equation length -- muzero might be able to exploit longer things. \n",
+    "2. Check how large the depth search is\n",
+    "3. Action masking, illegal action.\n",
+    "4. Debug the env... there is some shenanigans going on... aren't illegal equations supposedly impossible?\n",
+    "5. Then curriculum learning\n",
+    "\n",
+    "\n",
+    "### x+b = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 85 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_alphazero/singleEqn/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Muzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [-0.09575000000000009, -0.1925000000000001, -0.026250000000000023, 0.0, 0.0, -0.1137500000000001, 0.49, 0.0, 0.0, 0.0, 0.0, 0.2774999999999999, 0.0, 0.22374999999999998, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0037500000000000033]\n",
+      "Min Rewards: [-1.02, -0.5100000000000001, -0.08000000000000007, 0.0, 0.0, -0.2100000000000002, -0.08000000000000007, 0.0, 0.0, 0.0, 0.0, -0.20000000000000007, 0.0, -0.10000000000000009, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.030000000000000027]\n",
+      "Max Rewards: [1.0, 0.0, 0.0, 0.0, 0.0, -0.030000000000000027, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
+      "Timesteps: [389, 477, 565, 653, 741, 829, 877, 965, 1053, 1141, 1229, 1295, 1383, 1451, 1539, 1627, 1715, 1803, 1891, 1979, 2067, 2155, 2243]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 389 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ax+b=0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [-0.16875000000000012, -0.20500000000000015, -0.16750000000000012, -0.18500000000000014, -0.15250000000000014, -0.12500000000000008, -0.1700000000000001, -0.12250000000000005, -0.11750000000000008, -0.15250000000000008, -0.1575000000000001, -0.20750000000000013, -0.1400000000000001, -0.14750000000000013, -0.1500000000000001, -0.14750000000000008, -0.18250000000000008, -0.18000000000000013, -0.15250000000000014, -0.23250000000000012, -0.1875000000000001, 0.09749999999999992, -0.13250000000000012, 0.12499999999999989, -0.14500000000000013, -0.16750000000000015, -0.15500000000000014, -0.18500000000000014, -0.13000000000000006, -0.17000000000000015, -0.16250000000000012, -0.17500000000000013, -0.18250000000000013, -0.16250000000000014, -0.17000000000000012, -0.20000000000000012, -0.16500000000000006, -0.16750000000000012, -0.20250000000000018, -0.14500000000000007, -0.19250000000000014, -0.16250000000000012, -0.20000000000000015, -0.15000000000000008, 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 1091 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_alphazero/singleEqn/a*x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve a*x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### a/x+b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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v5cFY9Vqvx2wabW53cgyMsdls+M9//oMBAwYYfqdNmzYYNmwYxowZ43lvwYIFuPbaa1FSUoKqVav6/Wb8+PGYMGGC3/sffvghqlWrFpW0ExERERERERkpKSnBoEGDcPLkSdSsWdPwexZvK4u/MWPGYOTIkZ7XhYWFaNGiBa666qqAOzaeHA4HFi5ciL59+yIjIyPeyaEA1q0TrcGdOum/Tmapmk9T6RjHW24uUFAAtG8PZGWF/nur5lEr5KH164Hq1YFzzolfGsKxfr34N5r7LhbLNCuUPLp3L3DsGNChA5CZGZ31FxeLWecbNQKaNo3OMqNh/XrRe925c2i/+/VX0VMY6u+syu0W+6JGDeDss+OThmB5dNcuIC8P6NIlvHK6Mlmh7I2Emv6tW8XjPRMhrx86JJ7Ucc45QHZ29Jdv1Wu9nkKTzzZMqQC/cePGyMvL83kvLy8PNWvW1O29B4CsrCxk6ZQ4GRkZls8EiZDGVGe3i8JWHibt61SQavk0FY9xvKSlif2dnh7Z/rZaHrVCHrLbxf610G4xRQ5Nj2a6Y7HMUJnJo9E6H1Tp6dbMC3LitlDTFO7vrMrtts7xMcqjav0n3mkMxgplbyTU9NvtibHPgdiUXXqsdq3XYzZ9KTWdRY8ePbBo0SKf9xYuXIgePXrEKUVERESJKTlu8Isc9wNR+GSjCh+BRhQ9CR3gFxUVYf369Vj/x/i433//HevXr0dubi4AMbx+yJAhnu/fe++92L17N0aPHo2tW7fijTfewCeffIJHH300HsknIiIiIkppbCQjiq6EDvB/+eUXdO7cGZ3/uIFk5MiR6Ny5M8aOHQsAOHTokCfYB4BWrVph/vz5WLhwITp27IhXXnkF//znP/mIPCIiohC43WKoZ6JhIEFERMkuoe/B7927NwI9BGDu3Lm6v1m3bl0MU0VERJT8GCx7JcK+SMQGGSIiCl1C9+ATERFR5UqEYJb88bgREaUGBvhEccQKV+rhMadIWSUPsUeYAOvkR9LH4xM9ybYvk217yIsBPhERJS1WYGKH+zax2GypdczC3dZk2keJtC2JlNZElcj7OJHTHg8M8IkshAUYERERpYpUa3giqgwM8ImIiChkHKKfWBhEkRUxXxJFHwN8IiIiChkr5kRERNbDAJ+IiIhCwuA+8XDEBVkR8yVR9DHAJyIiopTAhgkiIkp2DPCJiIiIiIiIkgADfCIiogTCXmiyEuZHShXJltcTaXsSKa1WwACfiIiIiCgJMTAiSj0M8IniiBfe1MNjTsmCk2ORlYVb1rKMrnzc50TRxQCfyEJ4kSOKLp5TscN9m3h4zIhSF8//1MEAn4iIiIiIiCgJMMAnIiKikLjdHKJPRERkRenxTgARERElnkQc7hmLNCfifiAiouTFHnwiIiIyjQEtEUUbyxWi6GGAT0RERERhYWBGRGQtDPCJiIgSCAMqIjKL5UX0JNu+TLbtIS8G+ERERESUVBi8ECUPns+hYYBPFEfaAosFWPLjMSYisq5kKqOTaVsocswPqYMBPhERJS1WaIi8eD4QESU/BvhEREQUkkQNFBM13VbH/UpEZB0M8ImIiIiIkhQbYIhSCwN8IiIiIgqbzRbvFBARkcQAn4iIiIjCxh5ia2MDDBnhuZucGOATERERESUpBnFEqYUBPhERERGFjT3ERETWwQCfiIiIiMLGHmJrs3oDDPMPUXQxwCciIiKikFk9cCSBAXT0cF9SImCAT0REREQhY7BDRGQ9DPCJiIiIUgSDcrIi5kui6GGAT0RERBSBVA1OOESfiMh6GOATERGRaakazCaLaAblzAtERNbDAJ+IiIgoTKke5Kb69hMRWQ0DfCIiIqIUEc2AnEP0rc/t5nEifWycS14M8ImIiIiIkpTVAzmrp4/ij3kkNAzwieKIBVbq4TGnaIh3Por3+il87M0lIkpuDPCJiChpJWMgmozbRJUnVfJPuNuZTPsnmbaFiMxjgE9kIbwYExHFTqqXsam+/USpjOd/6mCAT0RERERERJQEGOATERERUVjYK0iUuHj+JicG+ERERESU8jgBIRElAwb4RERERJTykrE3Mxm3iaKDeSN5McAnIqKkxkoMEZF1sYymYJhHQpPwAf7MmTPRsmVLVKlSBd27d8fq1asDfn/atGk499xzUbVqVbRo0QKPPvooSktLKym1RERUWTjclohCwTIjfhjAEUVPQgf4H3/8MUaOHIlx48Zh7dq16NixI/r164cjR47ofv/DDz/Ek08+iXHjxmHLli2YPXs2Pv74Yzz11FOVnHIigRe01MNjXnnkvmalPfqYj8nqmEcpFpivKBEkdIA/depU3HPPPRg2bBjat2+PWbNmoVq1apgzZ47u91euXIlLL70UgwYNQsuWLXHVVVfhtttuC9rrT0REiYsVMiIyw+1meUFEiS893gkIV3l5OdasWYMxY8Z43rPb7ejTpw9WrVql+5tLLrkE77//PlavXo2LLroIu3fvxoIFC3DHHXcYrqesrAxlZWWe14WFhQAAh8MBh8MRpa2JLpkuq6aPvFwu8ScPldMJpKV5XyezVM2n2mNOseNyiXOqoiK8/W3VPBrvPOR0evetxXZNULEoY10u8W889kUoeVQet3DPBz0VFdbMC+GeI/E+t6LNCscnWB51OkWjSjTzZaw4neJfq6fTiJq/1f/bLd7dG4uyS2XVa70es2m0ud2J2VZ58OBBNGvWDCtXrkSPHj08748ePRpLly7FTz/9pPu76dOnY9SoUXC73aioqMC9996LN99803A948ePx4QJE/ze//DDD1GtWrXIN4SIiIiIiIgogJKSEgwaNAgnT55EzZo1Db+XsD344cjJycFzzz2HN954A927d8fOnTvx8MMPY9KkSXj22Wd1fzNmzBiMHDnS87qwsBAtWrTAVVddFXDHxpPD4cDChQvRt29fZGRkxDs5FMDq1UBmJtCpk/7rZJaq+TSVjnG85eYChw8DHTsCWVmh/96qefTnn4H0dKBz5/isv6ICWLsWqF0baNMmPmkIVyzOvzVrRA9k167RW6ZZoeTRSM8HPSUlwMaNQPPmQLNm0VlmNKxfD5SXAxddFNrvNm4ETp8O/XdWJc/VOnWAc86JTxqC5dHcXGDnTqBLF6BWrTgkMAQ//yxGAF14YbxTEh712rF1K1BYKMotq/fgHzoE7NsHtG8PZGdHf/lWvdbrkSPJg0nYAL9+/fpIS0tDXl6ez/t5eXlo3Lix7m+effZZ3HHHHbj77rsBAOeffz6Ki4vxt7/9DU8//TTsOjk8KysLWTpXwoyMDMtngkRIY6qz28WfPEza16kg1fJpKh7jeElLE/s6LS2y/W21PBrvPGSzRWe/xkMs9p3dLgL8eO4LM3lUng/p6dFLa3q6NfNCuMc53udWtFnpXDXKo2lpIp3RzJexkuj5Q02/9v9WFouyS4/VrvV6zKbP4ofUWGZmJrp06YJFixZ53nO5XFi0aJHPkH1VSUmJXxCflpYGAEjQOxWIiMgAZ88nolSXCNXbREhjMuJ+T14J24MPACNHjsSdd96Jrl274qKLLsK0adNQXFyMYcOGAQCGDBmCZs2aYcqUKQCA66+/HlOnTkXnzp09Q/SfffZZXH/99Z5An4iIiAJjxdCL+4KIKLZYzoYmoQP8W265BUePHsXYsWNx+PBhdOrUCd988w0aNWoEAMjNzfXpsX/mmWdgs9nwzDPP4MCBA2jQoAGuv/56TJ48OV6bQEREFDJWdogoGXCkFVH0JXSADwAjRozAiBEjdD/LycnxeZ2eno5x48Zh3LhxlZAyouBYSU89POYUKeYhouDCPU94flUu7m+i6EvYe/CJiIiIQsFgglIJ8zupmB9SBwN8IiIiIiKqdByiTxR9DPCJLIStq0TRxXOKyCsW5wPPMaLExfM3OTHAJyIiIiIiIkoCDPCJiIiIiJJQovTQJko6kwn3efJigE9ERERERESWxMaI0DDAJyIiopCwskVERGRNDPCJiIiIiIiCYOMmJQIG+ERxxAtF6uExJ4ofnn+pg8eaiFIVA3wiIiIyjYETJTPmbzLCvEGJggE+ERERUQpggEJElPwY4BNZCCtfRNHFc4qIiIhSCQN8IiIiIiKiJMdG79TAAJ+IiIiIiIgoCTDAJyIiSjDshSGiZMHyjCi6GOATERERERERJQEG+ERERAmEvV0UCeYfIqLkxgCfiIiIiIiIKAkwwCciIiIi+kOyjHJIpO1IpLQSWR0DfCIiIiIKCwMzosTF8zc5McAnshgWtkREREQUTaxfpg4G+ERERBQSVhSJEgPPVTLCvJG8GOATEREREVFcMNAkii4G+ERERERERGRJbAQKDQN8IiJKaqwYEBFRNPB6QomAAT5RHPFCkXp4zInih+cfEYWL5QclCgb4REREZBoruf64T6wn3GPCY0lEiY4BPhERERGFhQExEZG1MMAnIiIiIiIiSgIM8ImIKGmxd5Eodnh+ESUWnrOpgQE+ERFRgol3JS3e66fw8LilnkQ45omQxmTE/Z68GOATERERERGRJbExIjQM8ImIiBIIKzrh474jIqJkxwCfiIiIKEWwkYOsiPmSKHoY4BPFES9oqYfHnIjI2lhOE1EiY4BPREREFKZUDwatuv1WTRcRUawxwCciIiIiQnI1DCTTtlBsMI8kJwb4RBbDwpaIrI7lFBERkTUxwCciIiIiSkJsjCMjzBvJiwE+ERElLVZgiLx4PpAVMV8SRRcDfCIiIiIioiSWyA0piZz2eGCAT0RESY0VA5KYF4iIKNkxwCciIiLTGCQTUSpi2UeJggE+URzxYpF6eMyJkk8qn9epvO1ERFbEAJ+IiIiIkgobHogoVSV8gD9z5ky0bNkSVapUQffu3bF69eqA3z9x4gQeeOABNGnSBFlZWWjTpg0WLFhQSaklIiIiIqtiwwARJbr0eCcgEh9//DFGjhyJWbNmoXv37pg2bRr69euHbdu2oWHDhn7fLy8vR9++fdGwYUN89tlnaNasGfbu3YvatWtXfuKJiIgSFIMgosTAc5VUzA+pIaED/KlTp+Kee+7BsGHDAACzZs3C/PnzMWfOHDz55JN+358zZw6OHTuGlStXIiMjAwDQsmXLykwyERERUVxEu3LPYIGihXmp8nGfJ6+EDfDLy8uxZs0ajBkzxvOe3W5Hnz59sGrVKt3ffPXVV+jRowceeOABfPnll2jQoAEGDRqEJ554Amlpabq/KSsrQ1lZmed1YWEhAMDhcMDhcERxi6JHpsuq6SMvtxtwuQB5qFwu39fJLFXzqfaYU+w4nWJfV1SEt7+tmkfjXU5UVIh87HQmXj6OxfnnconlxmNfhJJHZTrDPR/0WDUvhHtM1N/ZE/4mVmscn2B51OmMfr6MlXiXvZFS0x/PcitUkV7Lg7HqtV6P2TTa3O7EbL85ePAgmjVrhpUrV6JHjx6e90ePHo2lS5fip59+8vtN27ZtsWfPHgwePBj3338/du7cifvvvx8PPfQQxo0bp7ue8ePHY8KECX7vf/jhh6hWrVr0NoiIiIiIiIhIR0lJCQYNGoSTJ0+iZs2aht9L2B78cLhcLjRs2BBvv/020tLS0KVLFxw4cAAvvfSSYYA/ZswYjBw50vO6sLAQLVq0wFVXXRVwx8aTw+HAwoUL0bdvX8+tCGRNK1cC1aoBnTqJ16tWAVlZwIUXxjVZlSJV86n2mFPs7Nsn/jp1Evs8VFbNoz/+CKSnA127xmf95eXAzz8DDRoAbdrEJw3h+uEHoHr16J5/P/8sesIuuih6yzQrlDyamwvs3x/++aCnqAjYsAFo2RJo1iw6y4yG9euBkhLgkktC+93GjcCpU8DFFydHD748Vxs2BM45Jz5pCJZH9+8HNm8W5Vm9enFIYAjiXfZGSk3/1q1AQYEotyx0edN16BCwezfQsSOQnR395Vv1Wq9HjiQPJmED/Pr16yMtLQ15eXk+7+fl5aFx48a6v2nSpAkyMjJ8huO3a9cOhw8fRnl5OTIzM/1+k5WVhaysLL/3MzIyLJ8JEiGNqc5mE5UIeZjsdt/XqSDV8qn2mFPspKWJ/Z2eHtn+tloejXc54XKJ/ZqWlnj5OBbnn90uAvx47gszeTRa54MqPd2aecFuF+kKNU3yd+npYpsSnZXOVaM8qu7zeKcxmHiXvZFS06+eI1bfnliUXXqsdq3XYzZ9Cds+mZmZiS5dumDRokWe91wuFxYtWuQzZF916aWXYufOnXC5XJ73tm/fjiZNmugG90RERETJwGaLdwqIiKgyJGyADwAjR47EO++8g3nz5mHLli247777UFxc7JlVf8iQIT6T8N133304duwYHn74YWzfvh3z58/Hc889hwceeCBem0BEKSYxZz0hokSXamVPuNubTPspEbaFDU/xlQh5hEKXsEP0AeCWW27B0aNHMXbsWBw+fBidOnXCN998g0aNGgEAcnNzYVduomrRogW+/fZbPProo7jgggvQrFkzPPzww3jiiSfitQlERESUwFhBJooMzyGi6EroAB8ARowYgREjRuh+lpOT4/dejx498OOPP8Y4VUTh44WOiIiijT2lZEWs85BZzCvmJfQQfaJkwAKLiIiIiIiigQE+ERElLbebjWhEEs8FosjwHKJEwACfKI54oSAiIiIiomhhgE9ERESmsWGSiIjIuhjgExEREUUglRs9UnnbiYisiAE+ERERhYRBHRERkTUxwCciqmQMjoiIiLx4XSSKHgb4RERECYaVYSIyIxHKikRII1EiYYBPRERERCFjYEaUuHj+Ji8G+ERERAmElTIiIkolvO6FhgE+ERERpQRWErkPiIiSHQN8IiIiIkoakTZisBGEiBIZA3wiIiIiCguDYSIia2GAT2QhrCgRUSJgWUVERGRNDPCJiIiIiIhSDBtrkxMDfCIiIkp6rMhSqmGeJ0pNDPCJ4kx7AeYFmYiIKD6S7RqcCNuTCGlMRom23xMtvfHEAJ+IiIgoTKx0EhGRlTDAJyKipMYAjIiIooHXk/jgfg8NA3wiIkpKNhsrBbHAfUpE0ZJI5UkipZVSGwN8IiIioggkSsU/FulMlG0na2M+IooeBvhEREQUkkStjCdquomIiMxigE9EVMkYZBARERFRLDDAJyIiSjBsJAof9x0RESUzBvhEREREREi+BqBE2J5ESGMycLu5r1MFA3wiIiIiIqIUwmA/eTHAJyIiSiCslJFVMC8SUWVgWRMaBvhEREREKSJVKsqpsp1ERFoM8ImIiIhSAINec7ifiCiRMcAnIqKkxYp6bHC/ksS8QERkLQzwiYiIiChpsNGByByeK8mJAT4RERERERFREmCATxRnauspW1KJiGKD5SulIuZ7otTDAJ+IiIiIKMkwuKdAEi1/JFp644kBPhFRJeIFiojIulhGE1GiY4BPRERJjRV2IiJrS5RyOlHSmWy430PDAJ+IiIhMY0XLF/eHdfHYUDQxP1GiYIBPRERERERElAQY4BMRVTL2AlQe7mtSpXp+SPXtJyJKBQzwiYiIEgwDtfBx3xFZB89HouhjgE9EREREKc9mi3cKiIgixwCfiIiIiFIee5Pjg/u98nBfpwYG+ERxxIKWiELFcoOIiIiMMMAnIiIiopAlY2NTMm4TEcC8nUoSPsCfOXMmWrZsiSpVqqB79+5YvXq1qd999NFHsNlsGDBgQGwTSERERGQR0a7kWzVoCCdd8h58q24TEZEZCR3gf/zxxxg5ciTGjRuHtWvXomPHjujXrx+OHDkS8Hd79uzBqFGj0LNnz0pKKRERK42UPJiXKRkxXxNRMkjoAH/q1Km45557MGzYMLRv3x6zZs1CtWrVMGfOHMPfOJ1ODB48GBMmTMBZZ51ViaklIqLKxgo7ERERpZL0eCcgXOXl5VizZg3GjBnjec9ut6NPnz5YtWqV4e8mTpyIhg0bYvjw4Vi+fHnQ9ZSVlaGsrMzzurCwEADgcDjgcDgi2ILYkemyavrIy+0GnE5AHiq3G3C5vK+TWSrmU7c7tY5xvDmdYn9XVIS3v62aR+Odhyoq/MuuROByxWbfuVzi33jsi1DyqNMp0hru+aDHqnlBHmuHI7RH38kyOtTfWZUVjk+wPOp0in+jmS9jJd5lb6TU9KvnSFpavFMWWKTX8mCseq3XYzaNNrc7Mfs3Dh48iGbNmmHlypXo0aOH5/3Ro0dj6dKl+Omnn/x+s2LFCtx6661Yv3496tevj6FDh+LEiRP44osvDNczfvx4TJgwwe/9Dz/8ENWqVYvKthAREREREREZKSkpwaBBg3Dy5EnUrFnT8HsJ24MfqlOnTuGOO+7AO++8g/r165v+3ZgxYzBy5EjP68LCQrRo0QJXXXVVwB0bTw6HAwsXLkTfvn2RkZER7+RQACtWAFWqAF27itcrVwKZmd7XySwV86nbDfzwA1C1KtClS7xTk/z27wd27QIuvBCoUSP031s1j65cKXpcunePz/pPnwZ++glo2hRo0yY+aQiHyyXK3OxskSei5ccfxb8XXxy9ZZoVSh7dtw/YvTv880HPyZPAunXAOecAzZpFZ5nRsGYNUFICXHZZaD3xmzYBBQVAjx7iWpzoTp8GVq8W5+o558QnDcHy6N69wMaNQLduQKNGcUhgCFatAuz2+JW9kVq1Slw7LroI2LoVyMsT21KlSrxTFtihQ8D27UCnTkCtWtFfvlWv9XrkSPJgEjbAr1+/PtLS0pCXl+fzfl5eHho3buz3/V27dmHPnj24/vrrPe+5/hhXl56ejm3btqF169Z+v8vKykJWVpbf+xkZGZbPBImQxlRns4mLhTxM2tepIJXyqdudmsc4XtLSxP5OT49sf1stj8Y7D8nhy2lpiZWPnc7Y7Dv7H7MZxXNfmMmj0TofVOnp1swLdrtIV0ZGaAG++jsrbU+45GheKxwfozwqh4dHM1/GSrzL3kip6ZflViLsd1l2xTofW+1ar8ds+hJ2kr3MzEx06dIFixYt8rzncrmwaNEinyH7Utu2bbFx40asX7/e83fDDTfgiiuuwPr169GiRYvKTD6RocS8aYbIunhOEVEqYtlHlJoStgcfAEaOHIk777wTXbt2xUUXXYRp06ahuLgYw4YNAwAMGTIEzZo1w5QpU1ClShWcd955Pr+vXbs2APi9TxQvvBgTkdWxnCKiVCQnYaTKx/0emoQO8G+55RYcPXoUY8eOxeHDh9GpUyd88803aPTHTTy5ubmw2xN2kAIlORZWRJSoWH6RZOW8IG+LIiJKJQkd4APAiBEjMGLECN3PcnJyAv527ty50U8QEVEA7AEgSj48p8nKmD+JUgu7t4mIiBIMK+zh474jIqJkxgCfiIiIUgKD+9jgfrU23qZAlFoY4BMRERERUVywgYgouhjgExEREVHY2ENsXQyeScX8kBpCmmRv4sSJIa/AZrPh2WefDfl3RERE5I8VNLIa5kkKF/NO/HDfJ6+QAvzx48f7vWf7o9nWrcklNpsNbrebAT4RERFRErJygBBu2qy8TUSpiudlaEIaou9yuXz+9u3bh/PPPx+33XYbVq9ejZMnT+LkyZP46aefcOutt6Jjx47Yt29frNJOREREccDKVuKK9rFLxmfNJ1v+TrbtIaLAIroH/4EHHsA555yD999/H127dkWNGjVQo0YNdOvWDR988AFat26NBx54IFppJUo6vOgSEVGi47WMiMg6IgrwFy9ejD/96U+Gn1955ZVYtGhRJKsgIiIiIqIwJdsICwoPG+JSR0QBfpUqVbBq1SrDz1euXIkqVapEsgoiIiIiIgpTIgR2iZDGZMT9npwiCvAHDx6MDz74AA899BB27NjhuTd/x44dePDBB/Hhhx9i8ODB0UorEVFC44W08nGfE3nxfCArYr4kiq6QZtHXeuGFF5Cfn48ZM2Zg5syZsNtFe4HL5YLb7cZtt92GF154ISoJJSIiIiIi8xg8E6WeiAL8zMxMvPfee3j88cexYMEC7N27FwBw5pln4pprrkHHjh2jkkgiIiKiSDDQISLySrQyMdHSG09hB/glJSW4/fbbceONN2Lw4MG44IILopkuopTFAowounhORRf3J1kd8ygRpbKw78GvVq0avv/+e5SUlEQzPURERGRxDKBIsnJesHLaKkO0t7+8HCgpCf2vrCzN8LPTp4GyMv3Pysujm/5IxTo/9e7dG4888khsV2IBixYtQrt27eB0Ok3/Rt3348ePR6dOnaKfsBjLz89Hw4YNsX///pivK6JJ9i677LKAs+gTERERWUksKumpHkgmEx5LfeXlwOrVwLJlof0tXw5s3lwPy5frf756NfDbb8CPP+p/FkqQP3ToUNhsNthsNmRkZKBVq1YYPXo0SktLY7djKtHcuXM922e329GkSRPccsstyM3NjXfSQjJ69Gg888wzSEtLA+Ddrnbt2vl999NPP0Xz5jYMGdLS896oUaOi8hj2li1bwmaz4eOPP/b7rEOHDrDZbJg7d27E65Hq16+PIUOGYNy4cVFbppGIAvwZM2Zg+fLleOaZZyqlNYKIiIiIiCpXRQVQVARkZgI1aoT2V6WKw/Cz6tWBKlWA7Gzf9zMzxfoqKkJL59VXX41Dhw5h9+7dePXVV/HWW29VSkBlltvtRkWoG6WoWbMmDh06hAMHDuDf//43tm3bhptvvjmKKYytFStWYNeuXbjxxht93q9evTqOHDni13E8e/ZsNGt2hs972dnZqFevXlTS06JFC/zrX//yee/HH3/E4cOHUb169aisQzVs2DB88MEHOHbsWNSXrYoowO/YsSP279+PKVOm4Mwzz0RWVhZq1qzp81erVq1opZWIiIiIiOIkK0sE5KH8ZWa6DD/LyhLBvN774aUvC40bN0aLFi0wYMAA9OnTBwsXLvR87nK5MGXKFLRq1QpVq1ZFx44d8dlnn3k+79q1K15++WXP6wEDBiAjIwNFRUUAgCNH9sNms2Hnzp0AgPfeew9du3ZFjRo10LhxYwwaNAhHjhzx/D4nJwc2mw1ff/01unTpgqysLKxYsQLFxcUYMmQIsrOz0aRJE7zyyiumts9ms6Fx48Zo0qQJLrnkEgwfPhyrV69GYWGh5ztPPPEE2rRpg2rVquGss87Cs88+C4fD4fl8/PjxuPHGTvj++/fQrl1L1KpVC7feeitOnTrl+c6pU6cwePBgVK9eHU2aNMGrr77qdwtBWVkZRo0ahWbNmqF69ero3r07cnJyAqb/o48+Qt++fVGlShWf99PT0zFo0CDMmTPH897+/fuRk5ODAQMG+XxXO0R/6NChGDBgAF5++WU0adIE9erVwwMPPOCzzUYGDx6MZcuW4ejRo5735syZg8GDByM93XequqlTp+L8889H9erV0aJFC9x///2efAEAd911Fy644AKUlZUBAMrLy9G5c2cMGTLE850OHTqgadOm+M9//hM0bZGIKMC/8cYbceutt2LIkCEYMmQIBg0ahBtvvNHnb+DAgdFKK1FS4TBAIkpkLMOIyMo2bdqElStXIjMz0/PelClT8K9//QuzZs3C5s2b8eijj+L222/H0qVLAQC9evXyBKlutxvLly9H7dq1sWLFCgDA+vVL0axZM5x99tkAAIfDgUmTJmHDhg344osvsGfPHgwdOtQvLU8++SSef/55bNmyBRdccAEef/xxLF26FF9++SW+++475OTkYO3atSFt35EjR/Cf//wHaWlpnuHuAFCjRg3MnTsXv/32G1577TW88847ePXVV31+u2/fLqxc+QU+++x/+N///oelS5fi+eef93w+cuRI/PDDD/jqq6+wcOFCLF++3C99I0aMwKpVq/DRRx/h119/xc0334yrr74aO3bsMEzz8uXL0bVrV93P7rrrLnzyySee+d3mzp2Lq6++Gg0aNAq6L5YsWYJdu3ZhyZIlmDdvHubOnWtqeH2jRo3Qt29fLFmyBICYRP7jjz/GXXfd5fddu92O6dOnY/PmzZg3bx4WL16M0aNHez6fPn06iouL8eSTTwIAnn76aZw4cQIzZszwWc5FF12E5cuXB01bJCJ6TF4070sgIkoFDIoqF/c3xRrzGJF1/O9//0N2djYqKipQVlYGu93uCbDKysrw3HPP4fvvv0ePHj0AAGeddRZWrFiBt956C7169ULv3r0xe/ZsOJ1ObNq0CZmZmbjllluQk5ODa665GuvW5aBXr16e9amB4FlnnYXp06ejW7duKCoqQnZ2tueziRMnom/fvgCAoqIizJ49G++//z6uvPJKAMC8efPQvHnzoNt38uRJZGdnw+12ewLhhx56yGc4+TPPPOP5f8uWLTFq1Ch89NFHuPhibzDqdrswatRcdOhQA9WqAXfccQcWLVqEyZMn49SpU5g3bx4+/PBDT/reffddNG3a1PP73NxcvPvuu8jNzfW8P2rUKHzzzTd499138dxzz+mmf+/evT7LUXXu3BlnnXUWPvvsM9xxxx2YO3cupk6dinXrdgfdL3Xq1MGMGTOQlpaGtm3b4tprr8WiRYtwzz33BP3t0KFDMWLECLjdbnz22Wdo3bq17iR+6uiFli1b4h//+AfuvfdevPHGGwDErQPvv/8+evXqhRo1amDatGlYsmQJatas6bOcpk2bYt26dUHTFYmIAnwiIiJKTTZbvFNAoWJjROpJtWN+xRVX4M0330RxcTFeffVVpKene+733rlzJ0pKSjyBtiSHUgNAz549cerUKaxbtw4rV670BP3PP/88rrkGWLduKcaOfdzz2zVr1mD8+PHYsGEDjh8/DpfLBUAEwO3bt/d8T+213rVrF8rLy9G9e3fPe3Xr1sW5554bdPtq1KiBtWvXwuFw4Ouvv8YHH3yAyZMn+3zn448/xvTp07Fr1y4UFRWhoqLCE2TK/NC0aUtUq1bD85smTZp4bi3YvXs3HA4HLrroIs/ntWrV8knfxo0b4XQ60aZNG591l5WVBbw//vTp037D81V33XUX3n33XZxxxhkoLi5G//79sW7dDMPvSx06dPAZxdCkSRNs3LgRAPDcc8/5NDj89ttvOOMM7339/fv3R2lpKZYvX445c+bo9t4DwPfff48pU6Zg69atKCwsREVFBUpLS1FSUoJq1aoBAHr06IFRo0Zh0qRJeOKJJ3DZZZf5Ladq1aoxfwpdVAL8/fv3Y926dTh58qQnY6vUew+IiIgo8aVa4EBE1le9enXP8Pk5c+agY8eOmD17NoYPH+65X3r+/Plo1qyZz++y/rjpv3bt2ujYsSNycnKwatUq9O3bF5dffjluueUW7Nu3Hfv37/D04BcXF6Nfv37o168fPvjgAzRo0AC5ubno168fyjXT/0drwja73e7Zvnbt2mHXrl2477778N577wEAVq1ahcGDB2PChAno168fatWqhY8++sjvHv/09AyfMtxms+nGcEaKioqQlpaGNWvW+ATWAHxGLmjVr18fx48fN/x88ODBGD16NMaPH4877rjD7z54IxkZGT6v1e2599578de//tXzmXYEQXp6Onr37o2JEydi9erVuvfH79mzB9dddx3uu+8+TJ48GXXr1sWKFSswfPhwlJeXewJ8l8uFH374AWlpaZ55GrSOHTuGBg0amNqucEUU4JeWluLOO+/Ev//9b7hcLthsNrj/yC02pWmfAT4RERFR/KVKw0wk25kq+yjZ2e12PPXUUxg5ciQGDRqE9u3bIysrC7m5uT7D7LV69eqFJUuWYPXq1Z5grl27dnj//cmoV6+Jp9d669atKCgowPPPP48WLVoAAH755Zeg6WrdujUyMjLw008/eXqSjx8/ju3btwdMl54nn3wSrVu3xqOPPooLL7wQK1euxJlnnomnn37a8529e/eGtMyzzjoLGRkZ+Pnnnz3pO3nyJLZv347LL78cgBhO73Q6ceTIEfTs2dP0sjt37ozffvvN8PO6devihhtuwCeffIJZs2Z53o/knKxbty7q1q0b8DtXXnklHnzwQdxyyy2oU6eO3+dr1qyBy+XCK6+8ArtdTGH3ySef+H3vpZdewtatW7F06VL069cP7777LoYNG+bznU2bNqF3797hb5AJEU2y99RTT+Hzzz/H5MmTkZOTA7fbjXnz5uG7777DNddcg44dO2LDhg3RSisRERFZBIfoUzIGwsm4TdFUVgaUlob2V15uN/ysrEw86/70af/3o+Hmm29GWloaZs6ciRo1amDUqFF49NFHMW/ePOzatQtr167F66+/jnnz5nl+07t3b3z77bdIT09H27ZtPe8tXPgBOnXyBuBnnHEGMjMz8frrr2P37t346quvMGnSpKBpys7OxvDhw/H4449j8eLF2LRpE4YOHeoJHEPRokUL/OUvf8HYsWMBAOeccw5yc3Px0UcfYdeuXZg+fXrIM7bXqFEDd955Jx5//HEsWbIEmzdvxvDhw2G32z0duG3atMHgwYMxZMgQfP755/j999+xevVqTJkyBfPnzzdcdr9+/TwTFhqZO3cu8vPzPfu+MrRo0QKHDh3Cu+++q/v52WefDYfD4TnW7733nk8DBACsW7cOY8eOxT//+U9ceumlmDp1Kh5++GHs3u2dQ6CkpARr1qzBVVddFdPtiSjA/+yzzzBs2DA88cQT6NChAwCgWbNm6NOnD/73v/+hdu3amDlzZlQSSpSsWJkgokTEsosodaSni2fVl5cDp06F9ldammH4WVGRCOiLi33fLy8X6zM5QjtAutMxYsQIvPjiiyguLsakSZPw7LPPYsqUKWjXrh2uvvpqzJ8/H61atfL8pmfPnnC5XD696b1794bL5UTnzr097zVo0ABz587Fp59+ivbt2+P555/3ecReIC+99BJ69uyJ66+/Hn369MFll12GLl26hLWNjz76KObPn4/Vq1fjhhtuwKOPPooRI0agU6dOWLlyJZ599tmQlzl16lT06NED1113Hfr06YNLL70U7dq187l//t1338WQIUPw2GOP4dxzz8WAAQN8ev31DB48GJs3b8a2bdsMv1O1atWoPec+FPXq1UPVqlV1P+vYsSOmTp2KF154Aeeddx4++OADTJkyxfN5aWkpbr/9dgwdOhTXX389AOBvf/sbrrjiCtxxxx1wOp0AgC+//BJnnHFGSKMewmFzu8O/RFepUgUzZszA3XffjZKSEmRnZ+PLL7/0bNjMmTMxceJE5OXlRS3B8VZYWIhatWrh5MmTfrMiWoXD4cCCBQvQv39/v3tSyDpcLmDpUqBqVeDii8V7S5eK58H+MblrUkvFfOpyATk5QPXqgDK3DsVIbi6wbRvQrRtQu3bov7dqHl26VFR6L700PusvKgJ++AE44wygXbv4pCEcFRVi32VnR/f8W7FCNHbEuL6mK5Q8uncvsH17+OeDnmPHgJ9/FvkgQJ2+0v30k8invXsDmtuDA9qwAcjLAy6/XDyLPdHJc/XMM4FodIaWl4vzKBQOhwPffvst+vXrp5tHd+8Gfv1VnJNNmvh+lp4u6kRWEe+yN1LLlonz4dJLgc2bgf37Rbn1x+3jARUXF6NZs2Z45ZVXMHz48IjS8fjjj6OwsBBvvfWWqe8fOABs2iTKriAj7cNSmdf6iy++GA899BAGDRoU1u/NxqERtYs1atQIBQUFAIBq1aqhTp062LZtmyfALywsRGlpaSSrICIiIgviEH2i1JKZGXrA7XAAWVlOVKsG6MVOVasCWVniXzOBJkWXUTfvunXrsHXrVlx00UU4efIkJk6cCAD485//HPE6n376abzxxhtwuVxh3ZaQqPLz8zFw4EDcdtttMV9XRAF+9+7dsWLFCjzxxBMAgOuvvx4vvfQSmjRpApfLhVdffRUXy65JIvLDIa5ElKhYfhFRNLAssaaXX34Z27ZtQ2ZmJrp06YLly5ejfv36ES+3du3aeOqpp6KQwsRSv359jB49ulLWFVGA/9BDD+HTTz9FWVkZsrKyMGnSJKxatQp33HEHADFL5PTp06OSUCIiIiIiIgpdKA0pnTt3xpo1a2KXGIqpiAL8yy67DJdddpnndYsWLbBlyxZs3LgRaWlpaNu2rennFxIREUWb283eISKJ5wIRJSqWX+ZFPfq22+3o2LFjtBdLRJTw5MWJFylKZInaaJKIaSYia2E5QokgogC/adOm6Nmzp+ePgT1R5HjxICKiRGHla5aV00ZEFCsRBfh//vOfsWLFCnz22WcAgJo1a+KSSy7B5Zdfjp49e6Jbt26WerQQERERUbQxkEweyXQsk2lbiMi8iAL8N998EwBw/PhxLF++HMuXL8eKFSswduxYVFRUICsrC927d8eSJUuiklgiIiJixZ2IiIj0ReUe/Dp16uCGG27ADTfcgH379uHrr7/G1KlTsX37dixbtiwaqyAiIiIiIiKiACIO8Lds2eLpvV++fDn27duHWrVqoUePHhg2bBh69uwZjXQSERERRYQjH4gqj9MJLF1qw7JlzVC9ug1XXAGkpcU7VdbVu3dvdOrUCdOmTYt3UijB2SP5cYMGDXDeeedh3LhxKCsrw2OPPYY1a9agoKAA8+fPxxNPPIFLLrkkWmklSnqsfBIREVG0xKte8fnnQMuWQN++6Zg6tSv69k1Hy5bifa1opXHo0KGw2Wyw2WzIyMhAq1atMHr0aJSWlkZnBXE2d+5c2Gw2tGvXzu+zTz/9FDabDS1btqz8hJHlRBTgFxQUwGazoW3btmjXrh3atWuHc845BzabLVrpIyIiClsyNppZYZuskAYisqbPPwduugnYv9/3/QMHxPt6QX60XH311Th06BB2796NV199FW+99RbGjRsXuxWGyO12o6KiIuzfV69eHUeOHMGqVat83p89ezbOOOOMSJNHSSKiAP/o0aP497//jS5duuCbb75B//79UadOHVx00UV47LHH8MUXXyA/Pz9aaSUiIiKylERq7EiktJJ1uN1AcbG5v8JC4KGH9POafO/hh8X3iouBkhKgtNR4eaHm2aysLDRu3BgtWrTAgAED0KdPHyxcuNDzucvlwpQpU9CqVStUrVoVHTt29DwNDAC6du2Kl19+2fN6wIAByMjIQFFREQDgyJH9sNls2LlzJwDgvffeQ9euXVGjRg00btwYgwYNwpEjRzy/z8nJgc1mw9dff40uXbogKysLK1asQHFxMYYMGYLs7Gw0adIEr7zyiqntS09Px6BBgzBnzhzPe/v370dOTg4GDRrk9/0vv/wSF154IapUqYLbbjsLc+ZM8DQwuN3A669Pxfnnn4/q1aujRYsWuP/++z3bCohRA7Vr18a3336Ldu3aITs729OIQtYVUYBfr149/PnPf8bLL7+Mn376CSdOnMDXX3+N/v374+uvv8aNN96Ixo0bRyutREmFFa3UxONORPEU7TIoGcu0ZNymSJSUANnZ5v5q1RI99UbcbtGzX6uW+P4FFwBDhgBt2ugvr6Qk/HRv2rQJK1euRGZmpue9KVOm4F//+hdmzZqFzZs349FHH8Xtt9+OpUuXAgB69eqFnJycP9LqxvLly1G7dm2sWLECALB+/VI0a9YMZ599NgDA4XBg0qRJ2LBhA7744gvs2bMHQ4cO9UvLk08+ieeffx5btmzBBRdcgMcffxxLly7Fl19+ie+++w45OTlYu3atqe2666678Mknn6Dkj50zd+5cXH311WjUqJHP95YvX44hQ4bg4Ycfxm+//YbHHnsLX389F5MnT/Z8x263Y/r06di8eTPmzZuHxYsXY/To0T7LKSkpwcsvv4z33nsPy5YtQ25uLkaNGmUqrdHCczI0UZlFHwB27NiB5cuXY9myZVi+fDl+//13AOI+fSIyxkKLiIgSEa9fZDX/+9//kJ2djYqKCpSVlcFut2PGjBkAgLKyMjz33HP4/vvv0aNHDwDAWWedhRUrVuCtt95Cr1690Lt3b8yePRtOpxObNm1CZmYmbrnlFuTk5OCaa67GunU56NWrl2d9d911l+f/Z511FqZPn45u3bqhqKgI2dnZns8mTpyIvn37AgCKioowe/ZsvP/++7jyyisBAPPmzUPz5s1NbWPnzp1x1lln4bPPPsMdd9yBuXPnYurUqdi9e7fP9yZMmIAnn3wSd955JwCga9ezcPfdk/DWW6Nx003itoUHHngE1auL77ds2RL/+Mc/cO+99+KNN97wLMfhcGDWrFlo3bo1AGDEiBGYOHGiqbRSfEQU4M+YMQPLli3DihUrkJeXB7fbjVatWqFnz5546qmn0LNnT7Rp0yZaaSUiIiIiokpUrRqgjNoOaNkyoH//4N9bsAC4/HJg925g/Xrg4ouBpk311x2KK664Am+++SaKi4vx6quvIj09HTfeeCMAYOfOnSgpKfEE2lJ5eTk6d+4MAOjZsydOnTqFdevWYeXKlZ6g//nnn8c114ge/LFjH/f8ds2aNRg/fjw2bNiA48ePw+VyAQByc3PRvn17z/e6du3q+f+uXbtQXl6O7t27e96rW7cuzj33XNPbedddd+Hdd9/FGWecgeLiYvTv39/TkCFt2LABP/zwg6fH3ukEXC4nystLcfp0CYBqWLLke7z66hRs3boVhYWFqKioQGlpKUpKSlDtj51frVo1T3APAE2aNPG5DYGsJ6IA/5FHHsF5552HG2+8ET179kTPnj3RpEmTaKWNiIiIiIjiyGaDp5c3mKuuApo3F8P09UZ42Gzi86uuEo/Mq1YNqFJF/Gt2HYFUr17dM3x+zpw56NixI2bPno3hw4d77i2fP38+mjVr5vO7rKwsAEDt2rXRsWNH5OTkYNWqVejbty8uv/xy3HLLLdi3bzv279/h6cEvLi5Gv3790K9fP3zwwQdo0KABcnNz0a9fP5SXl/ulK5oGDx6M0aNHY/z48bjjjjuQnu4f0hUVFWHChAkYOHAgAOCnn4D0dKBLF+D06SrIy9uDv//9Otx3332YPHky6tatixUrVmD48OEoLy/3BPgZGRk+y7XZbHBz+I6lRRTgFxQUoFatWtFKCxERUdTwgS5ERJUrLQ147TUxW77N5hvkyzJ52jTxPSC2t3nY7XY89dRTGDlyJAYNGoT27dsjKysLubm5PsPstXr16oUlS5Zg9erVnsC3Xbt2eP/9yahXr4lndPLWrVtRUFCA559/Hi1atAAA/PLLL0HT1bp1a2RkZOCnn37yzHx//PhxbN++PWC6VHXr1sUNN9yATz75BLNmzdL9zoUXXoht27Z5GjwOHBAB/tlnA5s3Azt2rIHL5cIrr7wCu11My/bJJ5+YWj9ZW0ST7KnB/aFDh7BhwwYUFxdHnCgiIqJIsYOBiKjyDRwIfPYZoOkkR/Pm4v0/OpQrxc0334y0tDTMnDkTNWrUwKhRo/Doo49i3rx52LVrF9auXYvXX38d8+bN8/ymd+/e+Pbbb5Geno62bdt63lu48AN06uQNwM844wxkZmbi9ddfx+7du/HVV19h0qRJQdOUnZ2N4cOH4/HHH8fixYuxadMmDB061BNkmzV37lzk5+d70qg1duxY/Otf/8KECROwefNm7N27Bd9//xGeeeYZAEDTpmfD4XB40v/ee+8ZNhZQYokowAfE4xfatm2L5s2b48ILL8RPP/0EAMjPz0fnzp3xxRdfRLqKgGbOnImWLVuiSpUq6N69O1avXm343XfeeQc9e/ZEnTp1UKdOHfTp0yfg94mIiIiIElW8GjoHDgT27AEWLqzAyJG/YOHCCvz+u35wH8s0pqenY8SIEXjxxRdRXFyMSZMm4dlnn8WUKVPQrl07XH311Zg/fz5atWrl+U3Pnj3hcrl8etN79+4Nl8uJzp17e95r0KAB5s6di08//RTt27fH888/7/OIvUBeeukl9OzZE9dffz369OmDyy67DF26dAlp26pWrYp69eoZft6vXz/873//w3fffYdu3brh/vsvxscfv4ozzzwTAHDWWR0xZcpUvPDCCzjvvPPwwQcfYMqUKSGlgawpoiH6//3vfzFw4ED06NEDgwYNwvjx4z2f1a9fH82aNcO7776LAQMGRJhMfR9//DFGjhyJWbNmoXv37pg2bRr69euHbdu2oWHDhn7fz8nJwW233YZLLrkEVapUwQsvvICrrroKmzdv9rsXh4goVtizXLm4v6PL7eY+JaLg0tKAXr3cKC4+gF69OnqG5cfK3Llzdd9/8skn8eSTT3peP/zww3j44YcNl1O3bl3PZHnSgAEDkJPj9tuG2267DbfddpvPe+r96b1799a9Xz07Oxvvvfce3nvvPc97jz/+uN/3VEOHDtV9BJ/0yCOP4JFHHvF5T84RAABLl4oh+pdeKobou93AiBGP4sknH/X5zR133BFwnQMGDIjLPfi87pgXUQ/+xIkTcfnll2PFihV44IEH/D7v0aMH1q1bF8kqApo6dSruueceDBs2DO3bt8esWbNQrVo1zJkzR/f7H3zwAe6//3506tQJbdu2xT//+U+4XC4sWrQoZmkkMosFF1F0JeM9+Cwnwif3HfdhdCXT/kymbaHoY+MmJYqIevA3bdqEqVOnGn7eqFGjmD1Goby8HGvWrMGYMWM879ntdvTp0werVq0ytYySkhI4HA7UrVvX8DtlZWUoKyvzvC4sLAQgngnpcDjCTH1syXRZNX0kiMeViD+Hw3vhcDrF62SXivnU6RTHWB5ziq2KCrG/KyrC299WzKOynIhnHpL7NdHKKlnORnvfyWMSj30RSh6V5U+454Meq+YFl0ukq7w8tIBM/i6a+yierHB8guVRp9P7r9X3eTzP9WhQy79EyuuxKLtUVrzWGzGbRps7gjEW9erVw5gxYzBq1CgUFBSgQYMG+P777/GnP/0JAPDUU09h3rx5OHDgQLirMHTw4EE0a9YMK1euRI8ePTzvjx49GkuXLvXMBRDI/fffj2+//RabN29GlSpVdL8zfvx4TJgwwe/9Dz/80PP4CCIiIiIiIqJYKSkpwaBBg3Dy5EnUrFnT8HsR9eBfccUVmDdvnt/9HgBw+PBhvPPOO7juuusiWUXMPP/88/joo4+Qk5NjGNwDwJgxYzBy5EjP68LCQrRo0QJXXXVVwB0bTw6HAwsXLkTfvn39nl1J1uF0AitWAFWrAhddJFonf/hB3B918cXxTl3spWI+ragQx1gec4qtAweArVuBCy8E6tQJ/fdWzKOynEhLA5S27Up16hSwahVw5pnAuefGJw3hKC8HVq4Uz9ru1i16y121SvSGXXpp9JZpVih5dN8+YPt28Qzs2rWjs/5jx4BffgHatxczpFvFL78AxcXAJZcAoZy6GzcCR46IYxmgapgwTp0CfvxRnKt/PNmt0gXLo7t3Axs2iHpPkyZxSGAIVqyIb9kbKXntuPhicW3ct0+cI9WrxztlgR06BGzaJK7lAeYUDJsVr/VG5EjyYCIK8CdPnoyLL74Y3bp1w8033wybzYZvv/0WixcvxltvvQWXy4Vx48ZFsgpD9evXR1paGvLy8nzez8vLQ+PGjQP+9uWXX8bzzz+P77//HhdccEHA72ZlZSErK8vv/YyMDMtngkRIYyqz28U9wna7qIC43b6vU0Uq5VObLTWPcbykpYn9nZ4e2f62Uh6V5YTNFr88lJ4u1p+Wllj5OFZlbLyPB2Auj0brfFBZNS+oxySUdMnrcjT3UTxZ6fgY5VE5aZ0V0hiMFc71SKjlXyLl9ViUXXqsdK03YjZ9EU2yd+6552LFihWoV68enn32Wbjdbrz00kt47rnncP755+OHH37wPIoh2jIzM9GlSxefCfLkhHk9AjStvfjii5g0aRK++eYbdO3aNSZpIyIiIiIiczh5HVH0RNSDDwAdOnTA999/j+PHj2Pnzp1wuVw466yzUKtWLcydOxc33HADtm/fHo20+hk5ciTuvPNOdO3aFRdddBGmTZuG4uJiDBs2DAAwZMgQNGvWzPNMxxdeeAFjx47Fhx9+iJYtW+Lw4cMAxKMqsrOzY5JGIiIisoZUDiKS8akSlPhS+ZwkipWwAvzy8nJ89dVX2LVrF+rUqYPrrrsOTZs2Rbdu3VBSUoIZM2Zg2rRpOHz4MFq3bh3tNHvccsstOHr0KMaOHYvDhw+jU6dO+Oabb9CoUSMAQG5uLux27yCFN998E+Xl5bjpppt8ljNu3DiMHz8+ZukkIiKKFitUiK2QhnhzOoHly4ElS8QcD5ddhpg/5zsSPGapicedKPWEHOAfPHgQvXv3xq5duyAn4K9SpQr++9//IjMzE4MGDcKBAwdw0UUX4fXXX8fAgQOjnmjViBEjMGLECN3PcnJyfF7v2bMnpmkhMsvpBHJygMWLgaZNxYRrdjsvxEREieDzz4GHHwb27/e+98orwGuvATGu9liOVa9bkaTLqttEFCk1bzOfJ6+QA/ynn34av//+O0aPHo2ePXvi999/x8SJE/G3v/0N+fn56NChA95//3306tUrFuklSnh6FcPnnwemTQPq1o1bsoiIyITPPwduusm/cnzggHj/s8+sGeTLIfqs1BOFLpHPGznaaPFioEGDxH0KAJkXcoC/cOFCDBs2zHNfOwA0btwYN998M6699lp8+eWXPsPiicgrUMXw5puBceOAPn3ikzaiZJTIlTKyHqdTNNDq5Ss5S/8jjwB//rM1h+vzfCBKLXqdSi++CDz2WPwenRgOll2hCTkSz8vLw8Wah3TL13fddReDeyIDwSqGADBzpvgeERFZz/LlvhVlLbdbPFt6+fLKS1M8JVOl2+kEfvlFzKmwbBmvxeQlb6tctAhYty5x8obsVNKWWQcOACNHAj/8EJ90hSqZypnKEnI07nQ6UaVKFZ/35OtatWpFJ1VESchMxfDoUWDDhspLE1GycjqBVatEpWzFisSpkJG1HToU3e+RNXz+OdCyJXDPPcALLwDXXCNef/55vFOWGqwcwMm88ac/Af/4h+ioSYS8YaZT6e23gYqKyk0XVY6wZtHfs2cP1q5d63l98uRJAMCOHTtQu3Ztv+9feOGF4aWOKImYrfAVFMQ2HUTJTm9IYvPmqTkBGkVXkybR/R7FX6LOqRCMvO966VIgPx845xxr3jZiZYmcN8x0KuXnAytXAv37V166qHKEFeA/++yzePbZZ/3ev//++31eu91u2Gw2ONl1QmS6wlevXmzTQZTMErlCRtbXs6doLDpwQL9nzGYTn/fsWflpo9Al+pwKRvQaOZ9+mo2coUj0vGG2U+nw4dimg+Ij5AD/3XffjUU6iJKemYph/fpAx46VnzaqXFYejpjIEr1CRpUjkvMvLU0ESTfdJPKTuiw5S/20acxfiSKUORV69660ZEWEjZzRkeh5w2ynUuPGsU0HxUfIAf6dd94Zi3QQJT0zFcMHHmDFkChciV4hM8MKjUNWSEM8DRwogiS920CmTWPwlEiSbU6FRGvkLC8H3n0XWLsW2LIFeOopIDMz3qkSEj1vBOtUAoAaNQCXS+QbK+SHYFL92hMKTnlPVIlkxbBZM9/3mzcHPv0UuPzy+KSLKBkkeoWMEsfAgcCePb7v/f47g3urCRYQJNucCon0lIfRo4Fq1YApU4BvvwUmTBCvR4+Od8qERM8bslMJ8HYiaZ06BVx/fWJMGkihYYBPVMm0FcPmzYGdO4G6dcUjWNau5YzfROFI9ApZKNiTEbpo7zNtj1ci9ICRL9nLaRQA2WxAixaJM6dCojRyjh4NvPSSf13H6RTvWyHIT4a8YdSppCVv32CQnzwY4BPFgVoRdDqB1q3FI1gmTwYeeoitqUThSIYKGVEspULDkNMpGsoXLxYzyAdqMFd7ObUScU6FRGjkLC8Hpk4N/J2pU8X34ilQD3gi5Q290UZaslx45BHrdjClQtkVTQzwieLs0CH/IXVsTSUKXbJV1q1IffTW6tXWrQxSapLPLH/gATH0+6qrgjeYy17OqlV932/ePPEmpEuERs433ghebjid4nvxFui2ykTKG2aueVa6fYMixwCfyIISoTWVyIpkhUz7uMlEq5BZkQyerr9eDKMdOpSjjcg65Ozx4TSYDxwo8rX09deJOadCIvQ679oV3e/FWqrNtxHv2zcoOhjgE1kUW1OJwjNwIPDmm97XX3yR3BWyyhBJ8EQUa8FmjweCN5irQe/llyfuSB+r9zq3bh3d71WGVJpvIxnmqCEG+ESWx9ZUotCpFbDLLkuuClll34sYjeDJKngfZ/Q4ncAPP4jbNVatiu/xT6TZ4yuDttf5ttus08h5//3By+O0NPE9qjxWuH1Dj9MpypelS0V5kwjXGStggE9kcWxNTR4MLigRMXgiLXm7xoAB4naNW2+N7+0a0Zg93ui+9USlBtFnnGGdRs7MTGDkyMDfGTlSfI8qh1Vu39CS5cxf/yrKmb/8hbeFmcUAn8iirNqaSkSpJVEevRWI0yl6gJYs4aNII2XF2zWiPXs8G2Nj68UXgccf929USUsT77/4YnzSlQrS0oAaNXzfs8rtGyorljOJhAE+kQVYdTIcIqJEePRWILIXqF8/4PnngREjUrsXKJLg1aq3ayTC7PHk68UXgX/9y/v64YeBkhIG97FmswF//7v39fz51rl9Q7JqOZNIGOATxVnjxtadDIcoUSXbcFtAVGZycsTzvdetq7zKTSIHT+wFii6r3q7BR2QmJvV43Horh+VXFrsS/fXsab3zwqrlTCJhgE8UZ9nZvpPh1KhhvdZUIoov2Qt95ZXA5Mmi96KyeqET4dFbetgL5C/SoedWvl1Dzh6fnu77PhvMrUvNjy5X/NJB1mLlciZRMMAnsgC1YpyRYb2KMhHFjxV6oa3+6C097AXykjNR5+QAK1aE36hh9ds1Bg4EzjnH+/q779hgnig470HlURtqrbjfrV7OJAIG+ERERBZlpV5o7aO3brzR2sETe4EEOfrj1lvFTNQDBoQ/+iMRbtdQhx/36sUGcytTyzUrBpoUH4lQzlgdA3yiOONFLfXwmJNZVuuFVoOlFi2sHTyxFyj6oz8S9XYNsiYG+JUvEfYz59SIHAN8IqJKIB/TtXgxH9NVGaw+BNEsK/dCW32/pnovUKxGfyTi7RpkTQzwyYgsZxo29H2f5Yw5DPCJiGJMDpHt2xd47jngoYdS+zFdZB57ocOX6r3NsRz9ob1d49lnrX27BlkTA3wKZOBA4N//9r7+z39YzpjFAJ/IYniRSy5WmCCNEleq90JHKpV7m2M9+kNtGGnf3poNJbyeWpt6fDiqjfSo5UqPHtYsZ6yIAT4RUYxYaYI0SkxW7oVOlOBJ29vcunVq9AKl6ugPo8awWP2OwscefAoF84h5DPCJiGLEahOkUWJK5V7oaFEbQLKzU6MXKNajPxicUTQxD1EwLle8U5A4GOATEcWIlSdIS3bJMsmepO2FTktLjV5oCl+sZ6JOhvOK4ouNRBQK5hHzGOATEcVIqg6RtZpkqRSogZjdnhq90BQZOfqjXj3f96Mx+kPtTePwdgoHA3wKBfOIeenxTgARUbKSQ2QPHNC/MNls4nNOkBZbyVgpsEJAlYz7NRkNHAhUVAC33CJef/EFcN11kTcQMTijSDEPxYcVrh/hYB4xjz34RHGmLbBYgCUPK0+Qlkp4TlGqU8uYyy6LTplj1fMqUYOXVKTmId5fXTmset6akchpr2wM8ImIYogTpMVfMlYKrBDEJOp+TdR0W00y78dk3jYrYQ8+hYKNQOZxiD4RUYwNHAj8+c9A+h8l7hlnALt3s+c+lpJtkj0tKwT4lNpY2aZoSsZy2qoS9frBPGIee/CJLIYFWHJSg/nq1RncVyaeU6RK1MptJGLR4JVs51Uq5ot4Yw8+UWwwwCeKM17UUg+PeeXi/iZVrPJDquUzBmcUKeahypfI+zmR017ZGOATEVUyXqQqVzLub/Y2UrxZ9bwK99zgOVX5GOBTKJhHzGOAT0RESS0ZKwVWCEaScb+SeTz+FCnOoh9/iXQeM4+YxwCfiIiSDifZI4otVrYpUuzBp1Awj5jHAJ+IqJLxIlW5GIjEBvNx4ohFgxCPP0WKAT6FgnnEPAb4RHEWToG1fj2weXPUk0JERGQKg7PEkQjHhw2xlSMR8oKRRE57ZUuPdwKIKHQVFYDTGe9UECWGZKwUWGGIfjLuVzLPqsffCueGFVj1+KjYSEShYB4xjz34RAkokkKupCS090PhdgOnT0dnWVZRVsbGlESXjJUCBjEUb2qPq1XzYzKe+2YlwrYzwDentJQjHADWxULBAJ8oAYV7ITxxAtiyBTh1yvf9Y8eA336LPDDft08sZ8sWEegng02bgB07ortMVmRij5PsEemL1vlg1eDMSmmJJ3U/WLW84Cz65mzeDPz+e7xTER88n8PDAJ8ohZSXi8LS4fB/3+kU/0bi9GmxfJdL3EaQDNxuoLg43qmgSLCCQKlODfCiFUjxvLK2RDg+Vm0ksppUroeo5RXziHkJH+DPnDkTLVu2RJUqVdC9e3esXr064Pc//fRTtG3bFlWqVMH555+PBQsWVFJKE8PRo2xFTQSRFHJ6v7XbRUAejWPvdsenED55Ugxji7ZwtyXQucSLlDkFBdFpKOL+JpXV8kNxsW/l/fhx/0bYaEr2HnwSEuGYJEIarSCV6+UsZ8KT0AH+xx9/jJEjR2LcuHFYu3YtOnbsiH79+uHIkSO631+5ciVuu+02DB8+HOvWrcOAAQMwYMAAbNq0qZJTbk3FxUBuLnD4cLxTQmaEW9DpXSiiGeAD8Qnyd+6MzZMFwtmOsjIxnO7AgeinJ1U4ncCuXcD+/ZEvKxkrBVYYcpuM+zUetm4Vf9Lu3SLvx0q0jptVe9ascG5YgZWOiRmpHMQGE+1jmUjnCAP88CT0LPpTp07FPffcg2HDhgEAZs2ahfnz52POnDl48skn/b7/2muv4eqrr8bjjz8OAJg0aRIWLlyIGTNmYNasWbrrKCsrQ1lZmed1YWEhAMDhcMARyyb2CMh0hZo+h0MEeQ5HbHsPSMoAALjdbjgcFZ7XgHxtzOUSBXSox0kG8fI4S06neF1eHtmxdzq9f9p1aIWbTwOtO5x9EovlVlSIIL+oSPs788c41Z0+LfahyxXeMa2osEFe4kR5Hfoyop1Ho0PkIZstnnlIll1OOByJVCuX558LDke0ZmvK8PzP4XCEVXF2uXxvnaqoMF8Wm82j0TgftMQtXRl/LL8CDoc1at9udzoAcSAqKsxvq9udBtnvFcrvrEo9Pi5X/M7VQHm0osIOIA0A4HRaJw/p8z3XK5PL5a2nhc+bfqfTCbnfrZ7XHQ5v2VVeHpu0WvNar89sGhM2wC8vL8eaNWswZswYz3t2ux19+vTBqlWrdH+zatUqjBw50ue9fv364YsvvjBcz5QpUzBhwgS/97/77jtUq1YtvMRXkoULF4b1u8OHxXPWKdb+DABwOouwYMFiz2uXqxwLFnxjagmHDoW3ZoNBLvj55/CWp2f5cnPfCzefGjl4MKqLi2i5x44B27er74hjXFFRiAULcqKRrKR37BiwcWPov1uzphGAiwEAa9d+j127wp9gItp5NDIiD7nd5suJWKXh+PGdWLBga5DvWok8/45jwYIVUV0mAHz9dWS3/GnL81Am1QqWR9esaQygOwBg9epvkJERebBXUFAFQD8AwJ49P2PBAoMLSyUrLOwNoBYAYNEi88fkwIHOAM4AACxdmvi3b5aX2wFcDwAoKPgNCxbsjmt69PLob7+1BnAeAGD37tVYsOBoJacqFN5zPV639+bmRvJrb/p37doFoA0AYMUKa+f1jRvrAbgMALBhw2IcPBiDezH/YK1rvb4Sk7NhJ2yAn5+fD6fTiUaNGvm836hRI2zdql/hOHz4sO73DwcYkz5mzBifRoHCwkK0aNECV111FWrWrBnBFsSOw+HAwoUL0bdvX2RkZPh8tm4dkJEBnHee/++KisQwwWbNgCZNjJdfXi6GQtepA7Rsafy9HTvEMjt1is5woAMHRGB67rlAoLaV9etFb0jnzpGvc/160XJ64YWh/SaU9aelZaN///6e13Z7ps9rPT//DKSlGafLKN1Hj4pKY5s2QO3a3vfz84G1a4Hzzw987IPZvh0oLBQ9UB06ADVqiPfXrRN5oFMn73e1+VTvO/K3djvQsWPgdf/yC5Ce7v/7tWvF+xdcEN42GS03kLIyYNUqsY/dbqB1a0AtLuz2mkGPcWWR+93tFtu4fj2QnQ2cc074yzx1Stwy0aQJ0LhxeMsoKBCB/XnnAfXrm//dunVA1aqA7MEDgM6d+6BtW5H/9+8HWrXyzf9GApWlleXECXHONmsGNGzofd9MOaEyOr9CpQ6RrFPnbPTvfxYAMQfG7t3+6dT69VfRExWN8jlc6el1wjr/jhwR1yGj/HPmmf3RoUPo6Vm3TuxXWV7/8ou4xrVvH/y3ZvOo6MEXunS5Gs2bh55OLfX2mTPO6IYmTdy6eUzvmmiUH6NRbxg71lu1vfLK/tiyxfiaXFgobodo2hRo1izN8/7ll/dHdnZ46w9FNOsrWuqTbOrWbY/+/dtGfyUGCgpEudWqFVCzpsijjRv3xYUX+ubRbdu8dwqfccZF6N8//j34enkzP9/3O02a9K/UMuynn0SZcP750Vle3bqtPf+/7LL+qIxwRu5XILQ8X7WqtyC44II/xWS/W+Fab5YcSR5Mwgb4lSUrKwtZWVl+72dkZFg+E+il0W4Xf3pJT/vj2paerv+55HaLkzQtLfD30tLE9zIyohPgy+UFS5/dLoLbaByecJZlt4sKrPnf2DTHSftafx1Gx1F+rpduo2Nst4vjGmiZZqSliWXI/8tl2e3evKAl86nNpr9+M3lNrkP7ezkXQLA8E4hRugKRz2qVx0G7fpst+DGuLHL7ZH4xu78DSU+PfDkyr4a6DLneNG9dHXZ7BjIyvOkKNT/Es7xX02z31oXhcNhgt2f4bGcggc7BUPg++zwNGRlpPuk0c10IrXyMBTsyMkKfgkgeA6P8Y7NlhLVd2vI6WPmuJ1ge9S3vw0unlm/eSzcsK/WuR0bfVa/z9tAPkWfZnlSlZwS8jqeliWtERobv+tLTo7OPgolmfUVLHcmrnquVoWpVsX5128T54V8n9aYxPc7lgpc2b6ZroqVwz/VwhVMPCbw8b16ozLwuz81QrgF61/JYSZTYzoyEnWSvfv36SEtLQ15ens/7eXl5aGzQZdS4ceOQvp/o1q/3f6652y1OrDVrRMu1lpkJLGRvXyAbNohWeLdbtNrpDSUvKxPpMDvzuQzWzDQWmNmOdeuAvDzR27hmjTco0zJ6X+voUbEcdf2nT4v3In38nJbcFxs2GA/Td7nEugNtm7R/v/c+0GikTTtZjpnlGn1Hfb+83DjP6P1ezgWQliby45o1vrOyHzki3tu50/+3x4+Lz8LZJ+okg4kyKYyZ479/P2B2TtJAyyosNM6XJSXecybUfaeWEepv1TIv0uOxcWN0Jv4LhdsNzJ/vO2KqqEi8/vxz88sItu0FBd4yLNBy9P6v91qPmetHqNasEbdyVIZAaQ93u7THRv2/ej4Esn69+WtVrGbRl6/1hhHrrTNYme92i20/cSK0dOnVEYzWJcu9eEw6duqU+DfS4yGv9SdP+r4faLmbN5sb7r11a/jPX5fzOcn1BMsDepPsyWt0aan4d+PG8Cag3LBBe7ucMTN5tTKv63Jd8joWbTt3evOQUefwnj3Ali3+7+/eHd5+NbP/9u0T+TTY74xiiUD174MHxWiyZJawAX5mZia6dOmCRYsWed5zuVxYtGgRevToofubHj16+HwfEPdbGH0/GWgvjPIi7HaLypyW2UIrWGVRthzLYE/v/uVTp8TnRUXm1hlq+oJxOkW6jh0T3zeqQJmd2fXwYe965W9OnhTvmbxlBkBoF46yMuN7w2XFxen0LfiMAuFozXyvF9hGGuDLyldJiUirXoXPaLucThHgHz8uvqPMmYmDB0XAr60Yyc8cjsgq7UbpslLQb1RJ15OXF1qDnJGCAt9JxVTqORNugK8lGwv0jnOoysrEfqhMixcDd9/t37Bw4ABw003mg/xgjhwJvs+NzmsZuAf7fbQDfHmNMZpXJNpiEeBrl6HuR9koFewaYnQ+GX03GvTKDodDNHYHW6dRXlHzkbyG6S0vkrRq35eT1lY2WfeI9HjI65x2GLnvaBvfz06fNrdfi4rCazxzu8W11e32/j7Yduo1UMl6lTwPyspCb/ABRNlvdjusFuCr64vFemVDjFFcAIj31Ud5SsePGzcKaIW6DUeOiHUGq0saxRKyLqHeqiIdOhSbxypbScIG+AAwcuRIvPPOO5g3bx62bNmC++67D8XFxZ5Z9YcMGeIzCd/DDz+Mb775Bq+88gq2bt2K8ePH45dffsGIESPitQkxFayQksGf+tpsS3a4lUCVHL5l9sIqlxOtC7G6vEC9l2Z7RVTywhqLyoO8aMrjpzdEV12/vPhLehcqpzN6Pfjq+rXrVINrrWC9OYB3eK/eMTEK8GUPvt535Od6ystFegPtE7nftNSKthUCfFnZMvpM5lP5Ws2veuVEMMHyu16QV1rqWwEIp0Jj9Ltwl2e0jsrkdAKvvho4Hz3ySPByymwgIb+jl7eDPUrTbN5wuaL3SCy5rMoK0IKdS9Fah0o++SSU31QGvet8NEarac/XYNsOeK+LgZYZaF3h5p9g6w0mmtfcUIJQs+dfqOmTx0qvvhbsWmhUhsnjE0kdxUz5J9NuZj2Vfb7J4xWLRwnKc1a7Tdq8bbTuUEcO6R0Lo7qU7HzSLsNo2dr39N6vqBD5KZy6fSJJ6AD/lltuwcsvv4yxY8eiU6dOWL9+Pb755hvPRHq5ubk4pIxfvuSSS/Dhhx/i7bffRseOHfHZZ5/hiy++wHl6M84liUAnw/Hj/rPlmyk8tD0MetTCyOh7Bw+GfmGN5kVJVgqD9TyFUhnVLifSyoNWYaEYaiaFG+DrBXDRHqKvt6xffgl8ETd6X6ZXzm9gNsCXAYm8z8/MBUT9bbCK5fr1wLZtvu8VFoqJotTAOR6Vb9W+fb75Rks9p7Vp3bHDt5yI1jmofi6H0skeavlZqJUZ9XwLpbIb6joq06pVgXun3W5xfM08tcLMMZF/69f7PpsdEO/t3au/vFB78KN5PCq7opabq98bGEnwoX2tvY7s3Suu2UaCnStqmR+tIEEOMZfLlI+01LsmBdtGo89cLjF6IVDjMCDKtwMHjD8PtC75KNRwrtMbNkQ2s3k0GrvkI9RCKfPM5tVQ0nbqlNgfstfVbH1SClY3iGa5obcO9ZbHYPuyMq8D0WygltS6jXxEtrahdMMG31Fj0Syztctat87/egNEFuAD+p1NGzbo142TTcJPsjdixAjDHvicnBy/926++WbcfPPNMU6VNQRqLVV7adTPzAaj4Qa82mWE2oNvNn2hFESBWoblRUoNEs2kUfv7aAX4p0/7pjVQgC/TEqwHXy4nWpU+vQuR/H9xsfkKoHzPbEuvUVrUhhy9dJmpVBiRlU9VUZHvxTPewT0ghqoF63k12t5Tp8LLG8F+o63YnT4tevGzswM3OARbptq4pb4frQpSZR9Ps7cDBHtkZqhlpzwmKofDfxiw3m8DibQnTquyG9BkXiouBurWje5y1eVrP5NlTZ06xssIdTRcpNT8oTZo6l0rA9VH9N5X90NFhQjwdeY79vlNoKHCwdYV7jXa7Y781p9olEl6IwmiEeCHQtZP5Mg3M3WfUAJ8s/UwM+vS+8zl8g73tlKAH4v1qtdnozK0okLcDtGihf9v1PSE2uhvVK/TXm/kiM1gIyIDvQ/4d2jJfFRRYVyHTgYJ3YNPoQsU4Kvvm1lGIHqVbL3v6F0Aior803X6dPDh0mbS6HB4AzKZRrMBvh6nU6S3uNj3PnZ1P0ca4Gv3h5pOdfi5Sk2vGuBrC9aKCu/ytftAbpuqvNzcfALaZek1Nqj3cxlNqKbXYCILZb3v6lHf1x7HQCMX9ALM4mLf4N1oRECwOQ3k+yUl5u+bjUR5ubmh1UZpVn8byjlo9F1ZidL7bjSC8UD3ectzsqBA/968YORyy8pExT6cifv0gpGKCv17HM3OASsfbxmoQUZNpzzv5X2K2u/pLaO83LgSbvbeelnelpbq7/+ystDujTx5MvxeGO35bIbcN3rXKam8XGybzBtGx1a7XL3/y9fB9m2wz9Wh82bz6+nT5ofcGzXIhXouq/lIvW6E0mAYyiR7kV6jo9H7HmjflJYGH70g6yixCPDDGUUl/zWTnkDXZ/U7styIlNH+lPnAbH6NNNAO5dqv7kvtPpBlTTCB1qU2zqm0nUNmriuBBKqX653jaWn+9b1Q9rtRHpTrSfYefAb4SSxYAas9oUNpyQ52UTJTaKsBtmrrVjFjp+q337zBZiSNEFu3imVpv2OmIULP77+LIdpbt/oG0WaWa/Qd7ett23xnjdXuW6PeErXQ1jvOgJj9VA4n1/YA7NnjP/x882bv/gtEL8CX78sKtbrsjRsDV8C0w0vNBvh6DQTBfhPo91u2+M4YGyzAN/qOujy9YWnRJAPJYOdrpOezWQ6HfoNOoIDfLKPt0C5v3z6Rv83kZb1lAeJpAjt3ivMn1Ane9u8X+UgNLrdt05+luEcPoEED43LZZhM9LD17iny3fbu5Wf7l+nbu9E1/sHwbKCA2E4iq69fb/5s2mX9SQ0mJGB4dbi/+1q3+ZVwwchtLSvyvU9LGjWLbZN7YulX/2KrL1FsHYP6WhmDnpdGtFYH89pvYFjOMztfDh0Nbp/yuev0yUxEPtI/MXN/DDdgiCfTMHNfNm4OfD7IRJJS0xeoefLX8CHb91b4XyyH68rdG+zPWAb3Wb7+Fdu036jSTZU0wmzcHXrbesTIz+jOUY6LNG9KWLfr1bPkIyUgaGbTfV9dTUVE5HSzxwgA/xagFpd69KaHcg292PYFOPG3LuTyZtS2sZkYEmCF7oLQVKKMLfLD1lpX5t/pqg4lIe/Blb5ekXVeg3opgjTylpca9+7LXVxvcme1J0QvY1AuGtmfIqIKiXU6gHne999SGJL3lhVIxdLl8W8v19oV6QQp2rrhc0X+EopZsGQ82UkH7f2061e+bEey811uvNu+EU7k0M0Rf3Seh0ju/Q60klJX55/fSUv3gOS1NTKIH+J/r8vW0ad7nebvd5vKUHEqrTb8aWBn9zoiZ46X2xJmtlBmJ5DjK9Yc7k7L26SRaMl0Oh3c/B0uL/FevzDbz+0D738wwV6M0mflcPYfVfGr0JJJg5a72fA0U4JtdltHnRp0NlcHMNcLMMoLdgx9pYBzK9+W/2vM8GgG+0XLMpAkIvD/V9Abal+GkQcvpDO3aH2jbzeSRYA1kevUD7W+MhuiHUp7orUfOAWC2Dmi0bKPf6g3RB7wTKScrBvhJTHtiqENC9SoMeidqXp5+5dXshT9Qy7i2YnbsmPf+Ze2FVl2eGYHSpxfgm6kEqE6d8j6qRXshk38nT4rH0ETSO3D4sH9wZSbYUivpegXkiRP+z+DVGwJnZtifdlnqeg8f9n1kolpRM3vx0DaQmCng5fBr2Xuk/c7hw75PI1AVFXkns9J+rnfOAKKXTl4otD34RheeI0f8l3fihPFjIysq9O+zPnRI/OlVFuR2qvng2DH/4cJ6+URvO0MRrNIarDKq/ukpLvZOdOZyiUk75XK1w8X11hfueakNPkKtAMt1G1Wo9O65v/xy4J13gGbNfN9v3hz47DNg4EDf9AVKu3Yb9Brx5HdCbQQ2sy/UciZaFcZwG1Hl+o2uc4HWGSwI0Bs5FWy5Mg/rLUt9XV4e2qMa1ZFT8jXgW27pLdtso4T8v9F+CXaunTzpP2GfupxgT28IJljZFk5ZcPy4KKu1vystDe2xfsHOKe1n8vG+2u84naJMVI+nNm3l5d7ROqGWW6dP6z9CraTEd8JJvbqQ2fWodYLCQu/cBmbqk8HolX3az/XKvrIy/RFawdIhr71G1DJLe+4HSrvRtQwIrRzTdqwdOKB/DTYzPD6UY2K0/+UwfTX98jqu7pdQe/D1vpcqAX7CT7JH5u3YoV/4SvK1PPFdLjGUtbAQOOcc/++FWpHUfq4tqHbv9j66wijAN3uxCBZcqP8GC/D13t++Xfy+alX9Sp7bLYa+hlP5l8rLxVBbo+UH2/96k8rJ1wUF4qKsjl7QBvhG+0UbmG/fLnoPu3TxXYfTKfKPepGWFRGjSqDedmhfB7rIy/d//924x17uV9kTr/3Otm3G+9fpBDIyfNMCiKG6NWsCbdua78Hft8//vZ07RbrVfal+v6AAqFcPyMwU7xUVeWeOPnUKaNPG+/2KCrGddrvvftu50/94ybQC+sGSXgAYjJmARltx0f420DK2bhW/qVtX7Be5H/R+a+b/ZqnnXyQ9x3rBc0WFOM4NG/o3al17LTB0KJD+x1W7Th2Rz7VzcJgtx9TjrfddvXMn0PKDfQaI82L1anHszjwTOP98/WWEul/DvY9SpnffPnH+nH22+d8Fqmiry9b7zMi+feLcNirzpd27RTD3xwODdL+jMrqPde9eoFo1oEMH72d79ohrfsOG5s5hyahcN7peqa+3bxflVJcu/vleXksibVgyEuhRqYHs2qU/78S2bSJIadDA3HJCTbts0K1VC6he3XcZbrfYl/K80i53506Rb+SxDSUoksOo69Xz/WzLFvG5nHAyUF3NqC4oqcdY3jooJ1ZUtzHUY61Xz9X7jl55t3OnqCto50IJlAZZxygtBVq21F+Xep09cUJ8Pz1dHJtA2yH/9K7R+/f719fNkCMT9fZToOMZ6P1A26D+K8kJ/U6d8p+81OwoFj16HURyeUajBpIFA/wkZ1TJMKoEq5UWeWEN9d4u7eeBWty0LXNy1sxQ16f9bqDvaydWC/RcZqMLitFj59QKidymUFudg12MQrkoByqQ1dZR+Vob4Mv0q0GEdpnl5aKhQ7t8tztwC2mgfaqXVu2yA3E6xcVSzWNqwB9oKJ6aFu36tBV4+X55uTfw0m6b0Tr08pxMtx614UAVKPBSt1f+lZd7K4babQyUVr1tMxLoGGnLGb3lmq3M6Z1nat5Wl6dt8Aq311e7v8MJLNSGLkndFm0FTn5PPQ8zM/WD+0Dp0Uuz9lgE2vdGlWAz6//8c+Dhh33nB2jYEHjzTf8RCKE0IhmV3aEIpZIaqMIb6LpnJo3l5eKYGjW0qnnYaDSRUZr1jptecBvJyJZA6TAqn+Q61blk1O0Np0FRL/gxSpe8NSbUbZZltbYcMSqnjZgZJaGmTW/mb/U8CJQHw5mwTC0LzDziVs0HwUaGaX+rTZu2vhrpKA69/wdan1xnsOuVVrA8ID+Xeb68XPzJzgOjtBuVjdrrfKjksdULhrX7TXuuqukzsx6ja0ugW3lCrX9o16d9z+wtlImOQ/RTiDYj61XstN/XVkKPHhW9B+qJUVIinmG5caO3sqBXIJWWiok+1EJf26AA+PbgHz4sehnMVPbNcjpFOuSy9Hp6jx8XLeF6lZaiIm+65cz+2kpfsABSpfe5HEKtd3FRlxmosNdbvxyGpf0O4J3QRAp0z/r+/eKY79rlPzRcvdDIC5dMa6AC3qjnWC9fBhumpQ2WtL8NdrEPNMrA7Ra9FqdO+c4nkJkp9ot22KZRhVlv+WbuM7XZxHrWrPE+/s4oWJXvy9/KGYS1FYlAF1D5W3XEgxlutzh/9W450OYB7WgXhwP46Sdg2TLgm298JzY8edI7OZF6odaWPXrrUz9Xj0tZmZh4Sbv/tbd4qKM7zJZJFRWibJTDW91uUWZq80WgJy+43eLcVYf+BmqcMJqXw+0WaXE4/MuoXbtEuoy2Ta8s1Fu+ns8/B266yX/yvyNHxPuvvRZ8EtWSEpHnd+4U5ffJk+L/aqV2717/YxZIoHLUzG8CHXvt/gjWAw34Xk/0yi01n+qV47t3i/2kXt/y8kSvvN6xNGrANxOYqctRfyO3Qx2hJJdlNKmY/O2uXd6nOqh/oQbBRnbs8H9PPrNdSx0G/9tvvrcQyDQHSo/KqHzR+26wz+Ux0zYqGF0r1f9rR9AFWvfWreIar60f6N0aou2okeszqmsYpdHpFMfo+HH9eozZ46z6/XdvmtVlaJ9iore+igrvhLB66z11Suwj7bGV+9rokX7aa7bsCAn0CEA17du2+Y6MDFY2A8Ebs42eqKV3DPTSpl7jTpwQZbUcVafdBr1zR28Ujd78WIHqfoGue3qvQ81LiYYBfhLTOxm1BbHeBUDN/NoAPzfX/4Io7zU6fdr7mVoJkgVHfr74XD5mTS5fmzb1PVmpDaWAD/QduX3a1kLtvti71/uII+3yDh3yfWSfXkVOG2CGWpAcPGi8vcEaD4wujEbpMerBVz/TLlvODXDsmP4tFWrDhF5gr5d2o8qPXvAZqCdB/sboufeBPlfTr31ck/rZyZPiXFDTY7f7zhgd6JiHsg+0n6v7X11fsMkWZb7U9v5ov2e0jCNHzOdjdfu08wYYVfzk/xcsAG69FbjrLmDqVODuu8UM8Z9/Lj6XjSjqI3TUfKK3LXoXdTVvHz0qyi/t44a0waL6eD+z5ZG871UG5xUVvunWpjFQg1Burve10fEOFgyVlfk+2k9+//Bh3wDT6Nw30xikcjpFz32g/TRlijcgNNqGQ4fEZydOiOuI2igk03r0qH+lMhA1z4TyDHmjXj21wUl7LTVz3TK611z+/tgxbyOdtix0u0X+PXLE915sOWxX71gF6rk2k2a9+oTeeS+Xozb2aa9LLpd37hPtdcrs/gv0mdvtXb7KKIBT55UpKdG/791ovdpyvKBAHDe9RyUGqyNoP9PO8aJ+J9CoDrfbO2LDzP4sLPRtDLDbRb1J7wkd2vXopduI9rfHj4ugXP1Me76ZvQ4BYt/L+pSaLu12aNPrdot9YHSuA2K5Tqc479THBwdqdFc/lwF9oHXopbGw0PsED+311GidgRoPAq3f6DqqvifrIvn54r09e8R+0ZsDSe+6Kcsi7frU5eulxyidwd5jDz4lHe0FU9tiplfpNqrEqO+pj7KQhYjeiWy3+074pV2WPIm1k+ypy4lkiJa6LL1WQaPgVrv9WVnGs8+r26JWBMNJp3bZep8b/dbo93oXSflvoCH62t9rtzPYNqhpMdqmQPvJ6B4qvfWpvVJqetXt0fZsG+Vxvc9kT5r6bHm95QXbXr1tDrY/1T85GY1RZULvOOnl6VDymt5MzUbk+vTSpa0cy3R9+y3wt7/5T1Ile3plkO9yeYcyyzSp52Ook+wZpVV7fLTlVbDjK7+nbQTTe0KFmg5t2aFXjhhV5ILlH6PgSa/cD1bZ0lu+1vLlgR/b53aLHrY1a4KvQ+4HvfM7lOuDXnkdSoBvtD5tRVQ9fsHOG/WYByq3jSq78jd2u/8963qNNYHSpd0/RtR1aEegqPULM+Wgdn/qnWeRcLv1G1CMtlXbWGNUNgRqkJHkMTHa12av5zJder2tevlRL38EeyKBuh41f8gA3yiN2v8bBY2BXrtcvg0QRueTVqDzXi9PGW2/Xv1I7odAPdx65YBaH9ZbjxrgB9sG7blfVqY/WjZYPgr0mbbM0O6HQGWs/L1a/9fL70ZpVa/fesIN8OVvjcrSaJQrVsYAP4kZVdDkv+qFs6REfxZNbQu/WmgXFYlKmZwYT57g6nq1FwjtjN5GF3S9NEeDmQqu3nrV/2dmigBfDVL0CsFglZpg6VQrfNr0a9er91v19eHD4lipF0/tMZLHTlID1rVrxfBJdV+oy7HZxPLlEF+9dGt/o02z/P727cCGDcbbAogLnPpcV22+ln9r1ohh3qtW+fb0yjSrv9Wmxej4u1zeXh91fgG9BgO5rD17fId4ar+3fTuwfr3ve/KYnT4t/i0u9q1Eqxd4dXvk/vn1V9+8EqiiLD+TEyYVFnqDMvmb/Hz/i74RNf8aNTxo87HTKXpyAy37kUe8+0BWmmWe1vZsaY+f9n1tAKRXIZPfcTrFMVCPm97+PHLEG6iuW+ed2FQtL+WwT708Jv82bBB59pdf9MtKrW3bxPE2U94YNdKpv5NDut1u7zOj9fab3rmjfU/vyQ96ZKNOsHJNHmv5nrbxyix1uLj83bp1YmRAMEbr05u7Q28iNpU87+Xv1GWotI1iRuWWvM4ajSaQy9LuS+26zOQl9XNto4s2n+i9F2jbtm3z7V0PlBbt5+p65HBvIPCjuLScThHQrl0rPj91yvd2M22atb/Vu5bK36xZ451AV35v7Vrj7ZOOHBHXWHmb4Zo14s9oFII6mkUea7cb+Pln/7Rv2iRuRThwQCxTuw3qfDqlpd5rktwfkssleuDlyEt1PXr7Sm00kAG+/K76O+2oxnXrxGenT4vbuYyeKmF0npw+7e0F11uf/L+2AVn7fXW/qtuhDeBVerdZuN3eoe16w9W1Zfbp096RqGauy6H24Gvzk955mJcnXsvHvsqRgXr7RF2OtmHwl1+AFSu8t/nKupusT2p/D4hRtvJaq9q1y/dcMjr+6vu//y7K4WTDSfZSiLYgUCciO33ae7+0+h29ioH8V/a0qz348nfq7yW9ngW97xoFV2Yu9Nrv6bW6q9ulnbxN+129z7TbYVQoqoFmsMqJ0TZo/+90igvb8eNigqozzzRenna52sYVNZ3abQa8DTcyr5w44b9s7T3Q6sR2cl1GLc3a/SaDwVOn/ANT7YVQDovT226XC1i6FJg1y/ei36SJ6B0+5xzvMvX2lZo/9Cql8v5lp9O3gqNul97x097zp+YLuc3qRbGgQLwnb3vRjioIFOCrM+Kq26NteNH+Xn6vvFwMB27e3HedoQZRepUcvf3kdosKa6D7p91uMYx7zRqgXTvf9Mr9Y1TxVtNhlO8DBdCyQqZuv1ymup6jR73nREWFdzZytWyT+ceoAiXzQVGR7wzSgdJXVGSuV06mwej8l++XlIg8kJWl/0hFI3p5o0kTc+mSs0er+8Eo70SjB1/OpSJf22xim+W9rcF+r3fNUBuZKir885ye0lLvssrLRUV33TrgjDOAzp2926ldll45A/jeuiLTpNfganSdMPO5ZLRMtT6gtzztdVebPtmIqg1gAlG/o677xAnfc9Lp9J2DxChQl+dqebm3jlNWJhr4tdunTYc2zdp7id1uUVbUru09tkZ5V11Wfr73Oi4DKnl7pF7ZrI6EUs+Pkyf9nwB0+rS3l157K5f8rQzG5fVFO2+Guu1Gc/hoaR/rp91P6nLVz+T+Ly8X109tfUCqqBDBW26uKGM6d/aenwUF3lnu9datvhco6NYeO3l8Ao2y0l6z3W6RnvJyb91c/UwuV74+dcp/3wU6T7KzjT8zKju1y9OuQ9ZRCgtFflLP60ANafJfvYlX33wTePVVoEULcZ5ql6U9F7TpOnHCv+6uzYPa43z8uPlrRyJhgJ/E1MqQ3n2qasCQni4KSHl/qKyEykD+1CmgRg3fHn2HQ/xOFkjqZ0YXOXkxqlbN93vagCRQ5UCP3Eb5+DCj7+kFOtoTXt7fqK3Ml5YCVar4F+raiqj6W7OVEy25T9R0fv45MGKEb49YvXrAxIm+j3fSVrTU46Wt3KtpUysZpaW+26JWQrSFpXwtA3zt+/JCF6jQlWlVv+tw+M+VoL3Qad8HgE8/BSZMgJ/Dh8X7jRuLAFFvmdpKiRrUyYuJTJPayCPzh3aZRhUA7T6Sy5DvORz+zwuXT5iQvUiygU0u32geBO2y5d+pU74VOPU35eWi8qkGH+oyiorEuWA06688n7WVGHW71T+XS//+WD1yJIH2T230kcGaHu25IRs11cDM7RbbJvef2uOuplkeo4oKEQjLyrEc4SPPGe1xUNOht1/0Gpi039fuV4dDNGytXCkasVq29J1roaJCjAwoKADOO8/7uCt1vWqjV1mZeNqCzA96eVgGG+rILfWak5kp5k9o3tx/hJi6HQ0aABdd5H1PzYdZWf5P/JDnoro/1VFVZujdHqatWOsJdD1yOr29TrI8lOkyOh9kWbdsmZhsUH3W+OTJonxv1sz7WzU/yPy2cWM9lJTY0Ly5qBjL/a+ev9qyRpZjMm1lZeKcVrcr2LVL/VxtdJFlud6IBnUfuN3ivNG7LpWVedMvy9fTp0UgIZ824HB4R9TpXScAb++iTKPRrYlaagCZmSnSXFIi6kFGDfzqb9X35fkjyydZnqvrl+WIzH/aBkX5O7XRXV4T1PNBu23q/+U2lZV5A3y5f7RlmnZElJpn9Jatzlmg5lXt/tWeA3oNrXr1Vb1GKkAcE/WYqmXP558D997r29DRoAHwwANA377+QbTedVLWSdQGem3ngLqf5HVIr4GypEQcM22dQH0d6LqlvS5oR6zplUeBJp9Vl602SMjGHu3v1HJC1ndkvpV5Q74vr6MlJaK+r9Yp3W5xrRo/3j9dhw6JOXgmTACuucZ/X6n/194GJBs8tPu2sND/cYt651ayYYCfArZt058BW1uYye8cPSr+2rb1ViLWrwcuvNC/cmG3e1u/As2gL9dXUSGGWqkTdh065J1VWv5Or8XO6EIMiKFlZWVA166+3zeaWVxvWfK1OiRV/sk0Xnihf6VQL41qQ0KgdBspKREzIsvfL1oEPP64/3IKCsTFqkED4Oab/dOtrlvtDdGm1+32BiP5+WLoWrVqvhc52Tuv/l6t6GiHhMq0y4qI3oVe/b/29a+/el/r3YOvd/wqKoDRo/X3qfz+xInAv/7lP6R+715xEVDzn9potWGDN9/rVQbkCAejfayXFr3Kjdst1lWtmu978jtyCLHaCKCX19XzT618A+JcX7/e+3jDigqR74uKREt/hw7imMnZuNV8DYjf1q/v21AiHTvmDeb0Gh70Kjdut3iuuxn16+s3Frlc3tEOZWW+vQLa46Hui40bxTOl5Wt5e0iXLt73duzwr9SrFbjjx8V2y1Evmzb5ridQQ4s2jWrFTW/fS+p+XbwYeOkl34rsk0+KgHHgQOOK7uOPAx07+q9LNtzKtPz6q//tV3J7duwAzj3Xd99u3SoqdF26iHz02mtiDgUjI0b4Nw46HKJcb9ZMNMqp6dMbfh7qo860jS2yAVpWBI0EOiYul7jeAmJZaWm+5YLR8hYvFuWS1oEDwPDhwNNPi4YSbWAxfTowfXo6Dhy4zPObf/xD/Obcc8XQU20gIH8rb3Fyu8U1+dAhcX1Tt1H+34h6ndYG83K9cr/If7Xp0D6ZQiorE42y8r3jx0X+veQScc5K2dn+Ty5RyXRob11Q06C3nfK8lU8dcbnECKLMTBHgqNukpc0beXni9eHD/j3napmyYYM4ZwBvnUZ7vdXmfdkpo91/2okV1XygLleWVfK76nwB2lv61DkMtGXw+vVAo0b6xzhQHtK7FmgbT/TKTPmdAwd887es7+7dK8oc7bqPHhWBZXo60K+fd78uWyb+2rYVZY7cD3LZap7Tpkdv/dp8ceKEuHbbbKJDRm+fyJG0RvtLXY86UatRHt65UzS82GyiYcqIzOt2u0jn0aNAq1b6x27nTjFydO9e306hsjLvdVBN65YtoizautU3H82Yob+dsix+/XXgqqv0t0vSzqkhO7+0ZdGePSLmAMSIMr26u9kRcImEAX4Sk5lbW9hoC0q1kFJn9VS/V1Li23ore1cyM8WJ7XDoX2S065AXTHVo6+nT+pVnbVoDXSjkKALteo32iXb52iBKLbjcbu/MxeqQKO0tCdp1y0LTTMug3j6T66qoEJX3QBfJ224ThaK8oOktTw4ZNepVkWmWLfqy8NTrXdH+Xq+3RhvgG61Tvq9eZNTf6W2L0T7dsCH4Y7IOHRLfk8GpTKccsqvmRb1nRKtBs/ZYayt2evlLux/UZav7U/uefK1Wvox68PWWrV1eSYmouC5dKobEyRlwARFEjx0LdO/ue7zl7+WwSD3q+WwU4KvLkv8/eTLwUFWbTfQEd+3qrbxq95ksg7QVeb0GIW0jnLYMUKkVPTXN6m8qKsT+VEdZyGXpBTh65ZlcrrouNX16++bzz/UbtQ4cEOXBqFHAyy/rV3RHjwaeeQZo3do3DXKkkkyL2rujbZRSh+nKP21v+sCBwGefAXfc4TvbdMOG4kkJTZv6D9uWx1AdequmSd2f8vgHKiNVan7RW26w3wa6vqjnpxzOrX6uPR+cTuCNN4yXZ7MBb70F9OjhfQ8Q5+3TT/v/5vBhEeQ3agR06+ZfJqnbIJcl7+vWu+4GolfGaM8Po++r55q23HM4REAlR5GcdZa3l1D+RpYV2ttTnE7f2frlKAe9EWhqugHfY6P2Ist/5fwZsiFYu03q9uuVm3IEo95+044eUW9RUNOknYROBvja/V1e7nuPstwP2nNF5nt5LVEn09Oe9263OB7a7QV8RwJoy8lAZV6guqm2TNa7jqp1FcB7b3qwJ3dMnw5ccYX+MPEGDYD77hN1hKVLxbBx9foIiMlDb77ZP6/L6592W+XEzGrHgnYkg95TFtT9ote4E2jfyqdp2O3Ajz8a7wu5bNnIWVZmXO9zuXzLd5dLNM7LPCMbxNQyVTsp9a+/+k+kq03P0aPiXvqmTfUDctlgp45e0dbd5fflqAOXy7eDRl2e2WtHIuEkeylC72SVfwUF3gJJb9iYLEi0hbcsBGSBAIiWanUyLu2JJgNI7cmlLagC3bftdosKiVoYqoWf+p4evcJXe1HRW6ecOV3dN3rbqK0UyPePH9e/j8sorbIH7ddfjSeQUbfp5puBefP80y2XrVewqZ/LglH2QurtF+0FVt0H6kVOXY/2PimjiqNRuvWCRL0eKSDwRUOlPpNcLk/dpvJycXFZvlz0TGgDAbWSpD3mgfKW+t6aNaLn7pdfvPlfrQjqDXFU97HePfh5ed4WdPUY6FW8KiqA778XQYK28pKfLyo9ixf7plk9vuo+Ky0VvYCyHFADMDk5lcsl9rv28ZNut6hEvfxy8MawadO8s+er6cjP91689c5LtYdPe57Lfao3ZFvNH9pyQ7setfKsFzzK7xpNPqX+X02LHA2h3eeyMvbww/r7Sv5m6tTAlZdZs/zzkrbxxWhma3U/FRT4lw2HD3vT3KsX8Oc/ez+fORP45BPg+uvFdwsLfW+FkP/KeTnUoe56PfhqgCrJx3jK/6vURmG1ISNYRS9QOabmI70GUvm4NbWheMMG//NPu76jR0VPqzp8eto0+Q2b3/cBMbxfbqNeua3uZ1nmyyBDr2zVow5b1uYZ7bkiX6u9fHpl0uLFwNChwFNPiWG6o0cDf/87kJOjf+6p5eTXX4u5C9TJ0/78Z9EzK9Ol5hHtrYHahjlZV5F/ctSU9thXVIieT0C/V1Vdt/r62DHfMkZ7jLT1IDV/yWU4nSKdmzaJkX4//wy8+64YmTN+vHd5770nytlg57d6e5P2vNdeA7THUBvMa+m9p9cQq7fvjK6r6pwE0tq1gZ/cAYge3TlzRCOo9rtHj4oRNRMnApMm6Z+fEyYACxf6l9fqdpw44dsopTfJsSw7Zf4CxPpkvVy7r9T8Ike46JXNgGhg/P57YMAA4M47fT9TzxE1zYA4L777DliyROxLvfqg9lyU54W2jux0in2tHkNtWWxE7ne98shoMlG5b44d855f2vqpmm7tdiQT9uAnOb3CVvv//fu9966r72svKrJwksGW7LWXF4QqVbwnlVyGvACrJ5n2UXmA/1AbNRCWjwRRe3PlbN9du4q0rV4tChE5qkCv8qVum1EQr/6uvFwMY9y3D2jTRtzbqJ2EUK+w1rvguVxiyH39+mLokzY9cviQyuEQ26zekxnME0+IoZzqsuW/ao+EbOlU0yp799UKkzZo1bvAqvlErXjJz7Q9IXrLUtMo84vs0VDzXKDC2O323lccTJ06+hMs2mzioqa9F7ZhQ9ET2rGjfyOIXIY24NPmCdXhw2JGeHX5I0aI4Zly2+WytA0nsjdK/iuXX1gI/PCDqJx06CAmFTp6VAw3lIH1qVPe86OsTPRiBPLqq6LnUM032u0FxHkiL+T16/se89OnxbDFunW9Q+jU87+iIng67HbguedET/Bvv/nu29xc77pkI5b2kY96s3FrK7l6eUpvv6vbr/5enWRSVnSqVBGTpi1fLoZ+nnOOb1BkVElWgwh1X2obCIM9gg4I3iudny9moZYTdqpp0faMqvtEfe/wYe/kRmqFa8sW0dtZtaoYLq5ue+fOvsFnebl3eLtaObTZxPvqdURbpmjTBYi8sHu3OLcaNRL/l1wusT61DFDXF0ygMszl8t4brW10OHxY7JOuXb2TDwYK7lVqZXfduuCNmYcPi8p5p07+11tJbURxOvUb34w4nb49xNp5EbR5WqZdfX63/Fd+Z+lSEVBpFRSIhsjx40VDkXrMZdrlPb1aR46IEQ3p6WJfqNskyxK1DJVOnvTefqieD9oAX6Z9+3bviAm9OoE23wLe56yr57m6f7WjBLWjLFwuEWS+8463gcFIcbEIWJ95RgyZVvehrF8B3h58uV/lqAJtgK+9zjkcoh62dav/BJGS0Tmj/Vy7b7X7cM0akf+Li70dE2rZaPac+vTTwHn87bcD//6FF4CPP9YPHt1ucb3dsQNo396bRu2tgXImesA7CuzgQbFd9euLuVTU4y+v+WVl4nqanW28DYsX658T8jM1vfJYL1okRoyq+3DyZFFfUW9RVdMkud0iH8h8LT+TI4Tkd8zejle3rn69U/6rbdhR96ss743ykrrt6r/JhAF+ilArjYDvv9rgWb6vrbzKgH7TJlEAVakCXHaZd6ItWUnT/l5bYZTrVGl78LUXMe3FRJ7U2uFVEyaICt1994mKgJa6PKOLsMslCr8XX/Qftvzii+K+LW3lUq+irm6n7P03oi2kAG9FsW5d499p5eWJx2vVq+d/DNXtVS/oTqcYJXDsmBgO1aqVf4ut3p9ej4S2MqRum3YfqfTSqO5DdbIwvcqQ/H/HjqIyH2jEQ+PGIgDWm/H122/F0HStI0e8w5kvvND/Vge9PKrdNr3WY3X5Y8eKXqurrw7cAOVy+U/2uGyZqIwEuz0BEHlj1CiRJ4MNkztyxPd2Bm25oZITXwH+vUyyJ1Gvd2vp0uDBisslJv1T06bmD+0wVe2jydTHW6nnl1q+BerBlxUXtfFQrZj/8ovoaW/aVNxfmZ4utuvtt30b7xo0AEaO9M7krFf+yJ557Xmk/h8Q3zP7CLpg9u0TlXLtvpXpUYMTvcqR2osmh+C7XL4TnGnzvPa6oDbQAN731EBDbfDS9kyrlV/5Ws0T2oaO0lL9SVmDVfTU/aJXsZbpUNOqbqe8/1kyW77XqeNdt9kA5uhR/0ZCuQ3qn0y33KfaIF3LaPbrqlW9wYx6HVfXqW1sUK9Jb74ZeHtmzhRlpFopt9lEvpk5M/hv33zTf3vU8179TJYJsnyQPYZ65THgnRRW3Va5HPlYvIYNRRkhP9P2dqrrl8fD6EkSLpfYnv/7v8DbrTVrFvDKK/4Nh7IeJ/dnRob3NhN1O/WCpGXLRCOtmi8bNhSTpekFhdrtkdQyVTvnj8slJhCdN89/LpE77hCdMJLZc0r7ZJtAadNz+LC4Pp51ln6AX1HhPxmddrvUfCbfk9cavXqybHBp0sR3tI32mDid4rGzZsh0/PyzaEjXys8XdZ8bbxR1B7k96nq1E6GqeUbb096+vahPa0d9STab+PyCC/xHMshlGl2P1P0qrweyTJPp0BvtFawhPBExwE9y6smvbTlUTwQ5e672fit5cjidwH//K4IQbdA7YoQImNxu79BDbQVRvqet9EiyMvbLL6JV/eyzxazK2uBIrSgtXSou9toC4sgREei3by+C8fJyMcGIHBqr18Mg38/NFUMB9e5tzM8Xkxe99ZY32FADCsA7U/XBg6LltWVLUciUlnofPZaV5X2KgLpNWnLZ550nLpZ6vfx69uwRFzinUwwvP3xY7E91oip54c7J8b//+rnngP79RW+j3e6duFB73LQVQO3n2l5oqaJCVAiWLLGhZs16ngYT7bGR61B71eTrdeu8EzZecAFQs6b38xtuED0aWjL/jx6tfy6UlBi3dktvvSUmh8nM1G851g6rV/ebmdsH3n4buPJK36BHHYYnl6XOSLt8uf4EXUYKCoAxY8QxNkPmDW3wLF/Li2h5uajg5+Xp97A6naICUVAgzoHzzxdlyqOPmkvHkSMiiNYOqwW8DZBqHlSPj6xIawN8uQwZlGqPm7oeWelVP1+2TOQHbZnYpw/w0Uf+23D0qNj3zzwjyredO0XFVB1KKG9BkAHvL7+IALx+fdGAJdls5h9BF4zsBdL2Tsp7oYuKREVaOzmU3Iey997t9j7eT+4ztZFA7R1fs0bst2PH/Cu9breYxEmtIMoAX65XBlsOh2h0Pn1alJOXXip6zWTDQHGxOIe0FbrSUt/eSXnLl9y+I0fEX+PG3ny+b59xhVoup6xMNJhu3SqOz2WXeYO0U6f8f3f++eLYBgra69f37QU0O6pLNgqolW2ZdrWcko+S279fP4A7dUpcV848Uwz51Zu8rKhIlEOPPgpce63/tUFbn9Ae72C3KgDi/Fm2zFtfkdu0fn3w8vXoUVG36NlTrC8317svANFYps3b2h73QE+ikXP0qJ/rNYQ895y4LeWss0S+OOcc8b7RfC9qQ5Kc8FjesvHtt4G3WU9+vhgR06CBd9u0s5HLslyWoeXl4naaXbtEY+9jj3m/v2SJcaP49Omiw0BO3giI8qRFC7Ee2XigrlvNmzJtGzaIa8dnn/mv5+hRcRtS48be8vGCCwI/uQMQdYZgAb4ZR4+K82L/fnGeAr6dG+o5oAbzcnvV76l5SZ10VOZVGZxu3CjSnpkpyhf1miwtX26uwV+uw+EQ9ZtA/v1vMQpm6FD/ep4aO6ijMOV31Oup3S4mh9Z72pFMz9//rj+xq1of0Z6Hav4pKxMjC/fsEeXgpZfqn9PaY5FMGOAnMb2KmPZk0Qug5L+yQFq3TtyP8/XX/uvIzxdB0WOPiR5zOQRJm4aKCjF8eNUqURDLRyK53eJEXL5ctCyrF/gXXxSFgJy9Xy28HA4RmAY6KR97TATYNpsozGXlSm8UgcslKvknTqj3NuobP160gGsDOb1e/3r1gLvvFhdFud59+4I/ikm9jcFuBx56SAQFZtSrJ4YFv/22f1ruu09cBCsqjCdpOnpUtJJLM2aIGbj/9CfjxhG150fSK9xXrxYtwOLCkw7gMsya5cbDD3tnN1WXuXGjd1bali2B//1PzPytN3y+uFhMVmVU0WvSBBgyREyso1745bpmzAjeiHL0qBhi26GD73BtuQz56KZNm0Tg0qqVGCIPiEAtmPx8UZnp0MG390hb8ZF/ZnqujKxYYe572tEg6oVcBj4yGAW855baCLB4sThf1R7nGjWMJ+rTU7euaNCRwaJaYVDnl9BWJgD/i7pMv/xXbZiU/8p1iAYlG/buFceybVvv0Pvnn/dPZ36+fnCveustUa79/rvIv/I8PXFCVEZ+/VWUlcuW+T6XXVYgpZ49g49YkQ0GRmWl3tMQXC5RoX7/fd9zrX594Pbbfb/ncvk+Rs/h8A/wtdcZQJRp6nIffdR3dJRaBurNyeJ0igbK11/3rag3bCgaYq+5xtsYJie+kuQytddFdR/t2SPSKxsa5KOytOeBauVK4J//9C13GzQQ29qjh/5EtHa7qMxOnuy/POmuu8T39IbPGqlTx9sgoz3+2vJEjoApKhJl7sGD3l5EQAw/dzhEo0WwycvmzROzX+sFweqx1fb4mR2VcOiQ93YSeVzM/lY2pBUVibJebZBVRxGqaVXLDXWOCHW7AO8+lNv9xReiZ1m7r44eFfd+S/XqietrjRpi+adPi/NaLV8B/SdhhEttkFPrRIB3MjK5T95+WwR2ch8sXCiulX/9q6gHvfpq4HW98ALw4Yfe1xUV3qc7qI12gG960tNFvVA7CsrIK6+I0VGAOK7BntwxcCAwd27w5QZTt653f8lOJPmnbTiR+1k7akRvDio12D95UryWIxi05cuDD/o+QQoQdRyz3G5RrzHTcPjKK775Wi1LfvnF28F07rkiL8lyX3t7S7CAWlvfkQLNzyW/v2yZGN2iHelx333ALbfoXw8Z4FPCUSvAmzaJVsaWLcWsybKgUYNytdLy3/+KINnMST93rmghU5+LLU+4lSvF52ohXa+eqIA1bChOwvfe81/mgQPe3q7u3X0vBOvWBS/09+8X3+vUSbxWK5put+/wOEC8Xr06+LDlgwdFa3aXLmKbN2wQM5R+/rn/9wsKxAWueXPRUycrvdoh5+qFwOn0PvlApq9LF1Fx/Nvf/HuiJPk86WPH9IdZFRSIexH79xeVzWD3PUuHDokWersduPhi48qNdpiZNhhdvlx/nQcPil71hx4S+8ntFo0P06f7HouHH9bPi3L4fCBnny0qx+vW+acTEGl7/31z+2P1arG8rVvFviks9N4rlpMjKhbqBTg7WwxtU2cqD0ROGCfTqb1/XH4GiHtszVZutQoLg/diNGggehhlAK0GaTIdsvdV5mGHQ+znggLvSJJnn/VfdijBfb16IgiVIwCqVRMNNZL6yEC9HnxtnpTpl++pz9FWP1+2zIY33rgKBQXeS6UctaQ3SsSso0dF74KcHVyWSdu368/YLGnfT0sTj8MzGgVhs4lK78svG6dl+HDfe23dbtFL+9pr+utXG0BlsKbOFq9W0tVzzeUyvqUgP180NmZkeIf0ykn11HuR1XXISfq0jhwRQ1MzM73zkajnDCDy/JYtorHzl19EwHHmmd5nyAPe4dHydhz1UZ9ypIW2UqjX4HP0qMj/Q4aIia70Au1LLhFl7MyZvmVcgwbiGtyqlWhQevFFvb3nhnaiPUDcPz58uKjUVqsmtlfSltOnT4ue2NmzvXlsyhTvLSV9+4rjsW1b8Dkfjh0TDVRyxIHcTvmvwyFGb8jGLDnviHoLTiDqSDQZ4Ju9p7d2bW8Z4XCIa7d8PGqDBr7fdTrF3BSbN4vP5C0xDod3lJV229TAbvRoc0FDQYF/407z5sA993g7QpYuFfWgaAUhtWv7BpHahie5b//1L9GoruVyiUbM0tLg9TAxjN3meeyavIaUl3uvHepy5d/SpaK+YlZenriOy1n+Bw4U99jfd5/+kP5rrhHbFu71ExCNqxdcIM41o9satQ0Y2jJR3sKRmyse19q1q+9M8PI7y5eL4Frr6FFRdrRuLdIDiLqoOsdPMC6X2AYzTpwQDVR/+pN47XSKxmhtw6bsULr8cu81Th5vh8P4ySHS9OliHdp9KOsZerf7ud2iHNMb0SgnTjxyxPdpYQ6H6FAxqlcnMgb4KWDRIlHBUwu5+vWBYcPE8B45vHXTJnGSt2njvSfYrIICcSGUz3CVLXU//iiGT+l9P1hAJU9c2dulVrzNtmLn54uCQD4CTa3kq/dCygpgsNnqpf/8R/wZ3YepNXmyqDzJ9aqTGp4+LSp4UlmZmBF/+HDvPALFxeLe7BkzgPvvN17PvfcGH4GwYIH4M0tu3/Tp4vhqe17UoWd6j22Rhajxo6BE5fT994HrrhMNQnoBYSiTDWrt3+/7TGU10KuoEKNHzFqyRFzQ1Ati3bpiWLZewFFUJFrdtY8XMiKDYrnvysv9H18pP4+kcgKI/PXf/xp/ft993lEJehWXTZtEA4A8xosXi7JGrfRp7yENx6WXivs51fO+dm1RfvXvL4KKQ4dED3vLloF78NU8AHiDSTVgc7tFgDtpUhoApTUO3lFLkSooEGmVvYrLl+tPMBZM376irH7jDf97YN98U1R0L75YBAzq7MX164sgpE0b3173igqjQNKfWiFXK6RqRVbue3mNCeSVV0RQAXgf76QG5zKgW7lS/1xTvfGGaBgGvI1v0qFDohFU+0glOZJA9rbLRg+5DXJ4dK1aonEglIDrX/8S5e6QId6e9WbNvOfTpZeK27H++lfx/csvF8F148aiAeKf/zRasvGsgLJRV2vxYrENubliZNOxY6IhWkveUpKWJgIZs7eJHTvmzRNyHo9jx0QZvGCBf8B1zz1in9erF7icb9BA7KfVq30r/vKe3mC3ObRtK3731Vei4URNxxtviMBaevBB/UbI118X99EPGQL85S/ePOB0iuMEiLrQwYOB91EgBw4A48aJRq8ePcQ1PVrBff36ondVHcGiBktyH5aVAfPnB17Wl1+aW6cczQaIciA9XYyYrFHDv8ELMDfxqp5Dh7ydV0VFonz94APv89Q7dxb5/Ngxce7fc4/5+9T1PPaY73wbcmSgtqF5zRpvI6h6HVq4UIzIURs+69cXI3quuUa8drlEg2uwdI4eLcqIL78M3uGhtXq1KA/MmjhRXFsef1zkF73rhSx7nn5aXKdl+V9Q4H3aViCnTok6obzdR/rpJ1FHatLEe71asUJsg3zsaiDaut6sWaJc+dvfbOjWzdz2JwoG+Enu22/FzOpa+fmiYLHZxMV79uzIK+XHj/sGAKWl0RkCJXu7LrnEWzianUSloMD3/k2Xy3eCMu2M+6EUcoD5i+7hw6KwuuwyeFqypRMn/GfAzc8XF6LcXFHpqFJFNHLIQl9LDlO32aIzhE+PrKidf773wvXrr+Li1KSJN7CSHA7x+f79IkgNVqAfOyYq0MEaKMJRWip6u2ULt1qh2bIltED59GnfZ3MDIu3BAg6jZ9yqsrNFflUnhZMTIGkDbLfbf8h2qC66SPxNmuQ7CWS9eqJSVFYmeuPl6BNtL63LJS7ELpe4yOr1METaMn7RRaJCrnXihOiB0fYw1a0rerW1PScyLWqFXO1xU+/3q6hQe72NA6hIyLKmvFysL1iPhpZ6v2+PHmI/XXed+Oymm8SImJ49xXbWrSsaSOQ6Ro4U5Wl+vhjOWKeO+H1mpqiEmW3o3L9fVF67dtXPG+q/GzZ4544wkpcnhoife67YL+rzlAHvv2Ya5I4eFcu68krj2aS1eVOOJGjTRpwDsvd+yRIR2KnXyPr1jR9RaCQ/X1RAGzUS6VInXJNDlqUGDbzp27bN/KOlzNA23ge73r/yitjnZq+7x46JHtgffvAfvad19KhobBk8WOTJQA2O997rTas6zNluF42RgRrI+vUTDUM//6z/6Mj8fN/rQKARRgcPitEaBQXifnqZ56PV8Kp2btjt0b2m9+kjgqT69cX+Tk/3bZiXjbkzZwav35it/8h8Ixv67HYx7FwG49L27WIUDRDeNsugz+0Wv5flulSzpm+jXvfuolx44w3zjVdaagOnmicBb9klbwFTv7tqlTiv9PLh5MmiXjV6tLg9Yty44Ok4cECUscECXD3/93/id6HcNnf0qEhfdnbg7731ltj+uXNDPy/+/W/RsbV+vfe9efPEX4MGoj59772RNaYBYlsmT07DE080MT03USJggJ/EKiqCD3F64w3R0qkVTqV83z4RDLjdItgvKopepUSdbdPtFj0dDRqIAiPQRebVV0VgPXKkqOzKRzFt3CiCtOJi732fgGjhD9YTEK7/+z/xJ3uJzP5Geucd/fvwb7tNjMaoVy94kBkpOSvz0qUiEFcvwnXritEFl14qKlEjR4Z+kTbz+KdwyVbqtm29QxSB6FacI1VUJILTevVEw1z79t4hjWvWiApQ8+beSS07dw4/v8qJu+RstXKOgL/+VYz6UfNe/fpiPgz56CO1Qitnj3733ci3X89vv4X2/WPHRMVDBruAf9CpHdqn/Xf9elnhi01wX7++mFxLVnjNTDCmJRuC5BB29faq5s1FRVZvki9ArG/ePP/760eMMNcQpS7n4YdFWdy/v8i3jRuLCboOHBBp6NJFbGMoj4Nr08b4WMmhwWaX5XQGv09Y69FHRW9YWpoo6/RGFOXn679vxtSpQO/evo0i2muubGhyuaIzGVggwa73eXniWF9xhchbgYbp2+3h3b7ywQfGn9WsKQL4Sy/1HRGiNtJddpnoSXzuOd8G85o1xfYFWn643nlHnGOjR4tbiGSeDRb0mJWfLzo3oiEzU/yp84PUry+u2bJH/9dfRd3N4TA/R0vVqv4N3qo6dYD27d2e71RUiNFCcl/t2uX97qJF4q9GjdC3r2FDkQfkdUyePyrtMHqnUzSOXnONd7RPKOSII+3TFrRlV1mZ2GY5T8PSpaLBLFDddc4cMalgKOf+hg3hNVQcOybqFkOHiobMUOjFD6r8/MC3iAVSWCjK7h9/9P/s6FExijaaZs8+D+PH+95qnMgY4CexX3+1Be2JCXZyhuLTT8VfLNSu7VvJS0sTFUszE8/JYYa33iqGnKktlF9+KZZ9113iflhAXPBCmZU8VLKXKFSHD4sKuNb//Z8Y6jVmTHgXxlDUqiUu/HrDOY8dEw1K3bqJAN9q1ABU9vJ27Bj8+cGxEOzWjoICUWl88knxWnt/W40aYmj2ZZeJCkqgXi8jd9/t7aFUzwm9RqL8fDHj7ejR3l4fWUHav18ExLFqKAm3jFKHZKu9yYD/rQZqz/3PPwd//nGk/vY37//LysJroElL81b+5T3i0tatIr/PmaOfzxYt8n9P3noweHDoadFOzKn65z/FUFaz90mrj4NTn4wAiNdmhwXLZYXTaLh/v6gsFxeHdh+wWXl53mfUa/OhJAMGt9v8voslmUcfeijwEOBY3MtaWCjyUUaGGP2g9pbKfWeziXt2MzJ8G9Bj3ThSUCAaY594QjRyyfmBatXynSAzXOEOzW/a1Ldns7zc/1G9+fmirtO/v2gAD6cc6t1bfwJm6fhx4Pbb03HXXU1wxRW+k2dOmiSCXa1Q5meR7r9fdN5o711X6QX8FRVihE448vJEkCmfhtCxo+8IUam0VAT4K1eKBgGz+znWeVe1ebOYK2jAADFBpFXoBfexYUN+fjUsWFCBgQMra52xxQA/iUVyz7KVyHvntPfRXn65GMpkdvIZo5mtT5wQPSonT3pn/000+fmiEi3v34yFGjVEMGJ8L6gQbnBfvbr5+9QjJXt5g/U+xIrZPDZzpn5l59Qp0XOkTuxodj6IevVEBbhvX/H7OXPMB0CzZ4ve/tq1fScBtGJZozYMaIeLV1SIWzO2bBEV8TPOED0/w4aZn2womGuuEffVq+moV08MOXQ4RB48+2xxvuzcGfryMzN9h37+9JP3s2++CT/d33wT2mM5gzlyRAQ/Tz0lKr+Bet/lsPSlS0XPq9or6nKJSqjZyr8crRPqCBDpq6/EEPNYWbRI5DcZiPz2m3/FX257u3YiyI9W3gzHoUMiPb16iSHD06b5BrBmy59wyXt6a9YUDSOy7PnpJxGg2e3eUXrxMHNmaGWpWbm54roYysiazp3Ftc3s0OVQ5uRRyUk8u3UT98wbNZYfPQq88EI3HDjgQsOGYhj+zp36kyuHa+ZMMZlk167eCVe1T0Y4cUKcdzabuIbJR4BGMvpMvV2oQQMxuZ2cOFKWW+XlYrSHUSNoNFSvLm6fVEfeheLLL8X16s47xRxQseqss7q9e+OdguhhgJ/E9u+PzfDSyjZ8uPhXtsyqjyq69FJxIZPPsY7E7Nki4NG28iaSWBbKp04FfpRT+NwAbCguFo0H2omvYikewX0oQunJUCsyb7zhnYwxLc07NPH558UcEC1bisposOfeahUUiKBYPk1B9qSZvTc3XtTK1g8/iG1XA9hggaeemjXFbQvayY9q1RK9OStW+Ab3WVkiII/WUOEDB8TjS//0J9Ez9NJL0VluQYG4T/f776OzPOntt0XArd5PqVVWJkYiSfXqiQbcs8/23spg1t/+Jiry9eqFl95YBveAGH7buLEINN5807+RTD7myukU59w554he1nhZskRM/uVyiVE8Var4HqvKahifMkWMuDt0SDTCqOfY//2fCPLioagouiMipTVrQv/NunW+EwbGSu/eYsREz56iAcr41kMbADfefz/N6AsRk/eEP/OMmN9CHTIvbd3qLa9jUc84elSMznzqKe/E0EuXimHmsb4VsLjY3JNxAjl2LPTbmZJNw4bxTkH0MMBPUqtWNcHcuVGYvjqO6tcXM2g6HKLnRrbMqpP5LVoUneBeimcPSTQk4ugD7X3OoV5069QRLeSh9HAkO/loSMA3wG/XTrTSjxgRfk/Tl1+KoYl/+pO4fzI/XwRRdetaaz4DlRz9s3y5/qPMQg3uAXHPpvaWmGrVRK+mvA9UVVbm7XGPBpdLDK/dvTv8Hjgj0Q7uAZFPgjWeaiumBQWih3DAABFwmx1VcMkloqK7dKloHGjQIHZze0Rixgzjz37+WTw//OefrZF2+fi7bt1EI826dfFJx/Hj+pN5StGsDySyYI8zjFTVqt4GHnlfeWCV0+E0bZpoNLvqKpEuo4meY9mJ8NxzYqRLnTqVO9z9n/8UDft61zgKxo369U+ja9ckuQEfgM3tTsyQIF4KCwtRq1YtnDx5EjVr1ox3cnSVljrQsKELp05lorIK1Wi74w5RaVV7NbKzxYy1f/+7qCivWiUquMn4/EoyJzNTBLBW74mvbK+/Lh7zBIh9JIet/vWvsZuIMTs7Nj1Y0VCtmuhtnzGDeSXZVakijrfa2BRJrxZ5/eUvYmSKFRocKL6efVbcJvnDD6JcjcXExJFo1EjMT2Ol+8krw6BBYl4AXudC5cYTT/yMu+/ujLPPtnaQbzYOZYAfokQI8MeNc2LixNgNhYq3rCwxKUy0Z9AkShbPPut9ZJTag18ZMjLEqBsiIko+/fqJyRZXrYrNJJREle36650YPvx/6NChf9IE+Byin2ScTuD11xN7aH4wZWUM7okCUSdtq8zgHmBwT5SMYj2JHiWOb78VtyHFa0JDomhbutSOoUPjnYroSu5IMAVNngwcP56Yw/KJKDrCnUmXiEgPg3tSFRSE9zg7IisqLLTht9/CnJHVohjgJ5HPPxePryEiIiIiIqLgVq9uHO8kRBUD/CThdAIPPxzvVBARERERESWOZcuaV/otjbHEAD9JLF8e+8eiEBGlFo5LJiIiSnYnT1bBzz8nzy3ODPCTxKFD8U4BEVGySZ6LPRERERnLy4t3CqKHAX6SaNIk3ikgIiIiIiJKPA0axDsF0cMAP0n07Ak0bx7vVBARERERESWOmjVL0bVr8tyWxwA/SaSlAa+9Fu9UEBERERERJY60NBdWr7YlzUR7DPCTyJ//DGRlxTsVREREREREieH48WoYNiwdjRqJx44nOgb4SWT5cqCsLN6pICIiIiIiSiwFBcCNNyZ+kM8AP4lwJn0iIiIiIqLwPfwwEnq4PgP8JMKZ9ImIiIiIiMK3f78YGZ2oGOAnkaNH5f+SZxZIIiIiIiKiypTII6MZ4CcJpxMYOVK+ssUzKURERERERAkrkUdGJ2yAf+zYMQwePBg1a9ZE7dq1MXz4cBQVFQX8/oMPPohzzz0XVatWxf+3d+ZxUhRn4396ZnZmz9ll711g2eUWWGABQeRYBASVGCMmBgWjxnhf4G2MMYZ4G+Ntbm/jK/lF42uMhoh4BUEJKqDxiAeKAiLHggjs0b8/nvfZfrqmuqdndmbn2Of7+cxnd3q6q6rreOp5qp6qqqurg/POOw927tzZjalOHi+9hO4kgiAIgiAIgiAIQnwUFgJMmZLqVMRPxhr48+fPh/Xr18PSpUvhqaeeghdffBFOO+00x/s///xz+Pzzz+Hmm2+GdevWwX333QfPPPMMnHLKKd2Y6uSRyW4kgiAIgiAIgiAI6cD3vgfg96c6FfFjmKaZcQu233nnHRg2bBi89tprMG7cOAAAeOaZZ+CII46Azz77DGpraz2Fs2TJEliwYAF8/fXXEAgEPD3T0tICxcXFsHPnTgiHw3G/Q6JZvhzgkENSnQpBEARBEARBEITMxOcD+OYbgGAw1SmJxKsd6s2qTTNWrFgBJSUlncY9AMDMmTPB5/PBypUr4eijj/YUDmWOm3G/b98+2McOl29paQEAgNbWVmhtbY3zDRLPQQcB9O4dgM8/BzDN5K3BnzatHZYvz+AhLUEQhE5MkD1LBEEQBEH6Q8SEhQs7wDA6II3MvE682p4ZaeBv2rQJKisrbdcCgQCUlpbCpk2bPIWxdetWWLx4satbPwDAddddB1dffXXE9X/84x+Qn5/vPdHdwIIFNXDDDQdCMhvpIYesgLVrx8JXX+UmLQ7BCyKI05kJEzbCW29Vwjff5KQ6KYIr0oYEId3w+9uhvT2VEwlq/8odXUVmCKki2Xqf1G3C718NTz+dnmuf9+zZ4+m+tDLwL7vsMrjhhhtc73nnnXe6HE9LSwvMmTMHhg0bBj/72c9c77388svhAmt7emhpaYG+ffvCrFmz0spFHwDgiCMAxoxphzPO8MH27YluqCZUVAA0NU2Ak0824OabExy8ECMiiNOZefOq4JJLTFiypB2WLPFBe7uUV/oig2XJQ/JWiJ329u7cHspeR4NBE1pbAfjiVcOwfxd6Gukkx9IpLbGSKWk34KabDoRx49rh6KPTr+GTJ3k00srAv/DCC+Gkk05yvad///5QXV0NW7ZssV1va2uDbdu2QXV1tevzu3btgsMOOwyKiorg8ccfh5wc9xm2UCgEoVAo4npOTk7UZ1NBIACwfbsJiW9IBpx7LkAwmAMHHQRw8skA996bwOAFIUuorAQYNSoAu3cDzJsHsGcPwJNPpjpVXSc3F2Dv3lSnIhlkgsKRqUjeCvHQffWmosKA6dPxOKyyMoDJkw0IhQB+8xuAzZsBGhoAliwxYOvWbkuSkHakhxybOtWANWsAdu1KdUriJT3y0RsGnHVWAI45Jv022vNqe6aVgV9RUQEVFRVR75s4cSLs2LEDVq9eDWPHjgUAgGXLlkFHRwdMmDDB8bmWlhaYPXs2hEIhePLJJyE3NzdhaU8H2tsBzj+fviW2IR1/PMD06bjpBADAKacA/PWvANu2JTQaQch4jjsOoKMDN2kBACguTm16OOEwgMfB3wiy07gXBKGnceKJANXVALW1AGPH4ilEOTnWJxgEmD0bZfi2bQD33JPqFAup5OSTU6/vlpcDXHopQF4ewJ/+BPC736UuLT2Fr74CuOYagJ/+NNUpiY+MPCbvgAMOgMMOOwxOPfVUWLVqFbzyyitwzjnnwLx58zp30N+4cSMMHToUVq1aBQBo3M+aNQu+/vpr+MMf/gAtLS2wadMm2LRpE7S3t6fydRLGSy8BfPYZQDJGyXr3xr9+P7qr5eQAnHVWwqNJC8JhNNJ++MNUpyRzOeoogLlzAUpK4nu+sLDraZg0qethOHHUUQDqWGRFBcCPfwxw8MEA+/djO/H5sK2kC+3tAEVFqU6FIGQn1dUAV16Jsk9IP8JhgKuvRoPtkEPQuC8qQrkYCACEQtZsnWGgoZ9so86XkVp4z6GyEnXBu+8GuP12nET77ncBevXq3nScd55VR/9vXjNphMPppbekkttvR/mQiaTVDH4sPPzww3DOOefAjBkzwOfzwTHHHAO333575++tra3w7rvvdm5G8O9//xtWrlwJAAADBw60hfXRRx9BfX19t6U9WXyRxP0gevXCjo8MfJ8PoLkZoLER4LTTAHbutN+fCe68EyYA/F+VgLlzAQ44AIV5TQ1A//4AH38M8NRTAMpqkKygpARgx47khV9aih4f554LsGIF5vPy5fZ6Yhim7cSH4mKAGTNQ8brqqq6n4dvfBpg2DV0tE+VeWVEBcPrpAOPGAVx4Ib7Tjh04E9TYiOs09+5FpbCsDGfyu9JRHnsswNNPA+zenZj0f/11YsLJy7O8eXoKoRAAO1Alqcydi8bFo4/arxcVZbJ7ZvZSWIhl9uMfA3zySc+e8S0uBjj0UIA//zkx4SWyzre0ANx5J+ov48ejLgMA0NZmzd5zA9/vx5nTRPP97wPU16Ou8cknAL/6VeLjSFfy8zH/Y+nTxo0DeP/9SD3TjUTJ6wsvxPT6fGhY19ZiP3/kkQCff44DRsnUdcNhgIsvRr3om2/QyE/2cpELLkAd/rLLkhtPJvDVVzh5Om1aqlMSOxlr4JeWlsIjjzzi+Ht9fT2YbFeUadOm2b5nIzU1yQm3vBxg+HBrRNswUEgXFaFBNmQIwOOPA7z5JgrBoUPReF69GuCuu7CBEJWVOMP5xBPR4y0uRoVpxw5Mw4UXJva9+ve3DPx+/QBmzsQO/ssv8ZrfD7BoEcDll2fGJju5uTiz/Pe/u7thV1Zi/r/1FsBHH6Eheu21iTX4d+/GjikQABg1CuDAAwHOOAPgww8xbbt2AUyZ0gavv74Stmw5CPr2DUBTE+bxhx92fdYE18FjWf7xj+jZ8sQTAP/4h/tzhYVYz8Jh7LzLy7FOb94MUFUFMHo0pnH/fny3xkasJwUF+LetDa8DWANhXWHePDQc/vtfTPuqVYkxrAsKMJyOjvieP/98fN///AfgvfcA1q7teprSFb+/A4491oT6ej9cd133xPniiwALF1rfzz4boE8fHGD60Y+6Jw3ZTDgMMHUqDuAmgt27AR54AGDiRGxXiVDAf/ADlFnxLqlJFaedhnpBUxPAddd1bXCyshJgwQKAW25JXPq+/BK9LK66CuCww6y+PRhE2e3z4QA1GfgzZ6Ls37w5cWl47jmA++9HHae6umtLpwAsfSyRaUwWJ5yAg+6xMG8e5s9NN3nv/y64APunxx+PPY2cmTMxTprg8vuxvvh8WH6JNO5POAH37Nm+HfWyqVMBRozAukmTayUlOBmVDCoqAE46CWDyZNRlysuTP5iQKmKZhEzm5GkyyVgDX4hkyhRUAjdutM+MdpXTTsOOjzpCw8AOr6wMf8/JARgzBoUOdZDBoDXDv3kzjlKHwzgK+d//4ogYN/x1LFqE4ZomjsYmkvJyNOqJLVvQ2AkoLeLQQ/HvHXegwUdUVKAC9stfJjZdXWHvXoBBg3DA5ec/d77v3HOxrJqa0JAIBLDjPPXUrsXPB0FoGYdh4CcUwr9jx6JgXbsW/zY2fgW7d5tQVYXpaGtLzGz1woVoxH75JYY7YQIqQBUVAA8/7Pzc2WfjvWQABwL4Tl9/jbPWZMT7fNa7UacfCKArF133+TB/H3oo/vfw+TDvmpqws29pQYN62TKA//3f+MP9+mtchvKnP8X3fN++2IZGjszepTpEe7sBf/qTAQUF8Yfh88U2mLJ1K8Cnn1rfJ09O/F4OsaZJx7x5aCSrbTbdB0QvuggHAMeNQ9nO+6KupH3xYnT/TgQHHYSDBplGeTnmX3MzGievvQawbh3K+yefRAPGK9RXbdnSNTmq4+67AWbNsmbxQyFrlnbjRrweCKBBdeedAN/7XuLi3roV4J13cEDoP//p+iDOBRegV8A//wnw/PPeNnXNzcV+LpoelmhiNYjLy3HmfvFi78+UlQEMG4ZtfMgQLGsveXzxxdg3dnQA/PrXeI3rtNSvUx8fz0TE8cfjYD03nMvLUWcbMQInWiiukhLUKXicAwfixo8VFdZkVFc57jhsr337Wl4PgQB6K15zTWLiSDdOPBHz7y9/iX5vsiZPk42s/ski/H6A226jb7FrKOpsY0UFwI034sBBZ6imJWj4czk5+Nfvx46ShCIZ/4ccYs12+nwoONw4+WR08aZ4YlW48vLcfx8/HoU+8fDDqKy+8AIacJ9+asU9YwYqKNdei6P+P/4x3j9+fGxp6g5+8xvM6/POi3QtLC/HzULI1Yjez+cDmDMH36+0NL54Kyrs7sS8IyQj1eezBopUeAdWVRVfGgDwHS+5BEfdadQbwIp3xAj353lHTmmmdPEPhccNfBrQoHbwyisAv/hF19zi337bHq/fj+3p4IPjD5Po3x/gZz+LvcwrK1EmBIOonCZLQaysxBnAPn30daar5OZiHNHByGMtx+Ji9L740Y/iM6R5fDTwOGmS1zQ7Q6e7xpImdXCB9ps45xx0R//lL3HA86ij0GX1Jz9JTpl1lbIylOFTp2I7nToVjehf/QqNpCuvRI8f3ufFwuefJ27GvTs39MrN9eZtFO0eWuK2c6clJ0ePxqVGEybEZtzTPiyBANYtmlBIFF9+iV6HAJZ3Ir0fyX2aNZ07Fw2waHvDHHccHlfshe3bMezt2+N/BwA0GA85BPufUaOw3/HCwoV4EpLXAanm5riTaCNWOXryyZax7RWarAgEUGbee68l95yorMR6dvDB6IVKkM7KdVzSK+JZvtG3L3pvLF6M3oK/+AXq7UccYU0UUNpJZ+JLY6ldnX127HE70dCAg50Uz5YtGNekSSjn0+xE8ITw17/iMgu3PYkMA8sr3v4g1cgMfpYxdy7Ao4+2w1lntcJXX0WxcgFdu8gYrKtDpeKDD7CzmzYNN9cjY5dmfFTXYxI4tE6JhJTOMCKlb/p0HMl96CH7GvfiYlS0jjnGUnBefjn2NY033wzw6qvonsVnl6qqcIT+6acjn/nyS9yl9Cc/wRmzvXtR6IfDmO5Ro/D5jz7CNHVlZLNXr9g69hNOQDfz5593v+/LLwHWr0dl6rDDMJ05OWjMhcOooHDFWy2TgQNx9mL5chSA0TjmGFTgJk1CJeP738frVA+oQ6qoQEGZnw+wYYMVN93L03TQQbG5hoXD2CnPnIkdld+P8ezdi/WJOkjTjL7W8Xe/wwEdqsttbVjnqePjaaZ3oA6/b180eOkePoAULz/+MebFGWcAHH64Pe6uUlGBM/ANDQCbNuFylWXLoi/VWLQIZcX+/eihEA8LFmB877+P3885BxWM0lKs562tOJBRXAwweDBuapRo5s/HWZ6LLvIygOhurVZVoVJZWGi5V44YgYbTM8/Elz5uVHOD46KLcBArVgoL0UiJdZlBRQUqyFu34rKecBgHN+mkiIICLLvx41F2FBai2/GwYbiUg8v3cBjL1s3NNhxGmeKlbhUVYZ93//3O95xwArahvDyU4WVldi8b8pDp3x/La9s2HKh46SXPWWSjVy/Ms61bYxuYnjLFHmf//t6f/eEP18L+/cPgoYfiO8/p8suxPK++2vmeefNw3fj11zvfc/75qDu0tkb2+bEuAdu9GwdcLrsMZd+pp7rHHQ/btlneZgUFuL4awD6gSm2vuRnr9COP4Aws1yvKy7GezZwJsGaNXr9QKS/HcL3uL1BQYDeOi4tRFh96KMrjXr0wXV4GXBcswD6TvNu8HHl87LE4eKPuC+IN69jmP//Zm5dMZSV6j4ZC3meqKyvRo2zcOKxv1G/n52Mfc+21zs8uXGgfpCcoDPJCJN0AAOVGWVlsg9zl5fblfUVF2P9yXcI08W99PQ6K5eWhFyzVS/KQueYagFtv7fpMPg1U8DZL9f7ggwG+9S3UqZ9+umtLElSvMVqG4HUZRX6+t0HCAQPQU9iNTZtQ/zj/fBxkUY8WJ7l1663pd0yeV8TAz0KOPtqEQOAf4PfPgfvuC7iudz/nHDwOxjSxwxo3Dl3Xd+xAIVRYiI2Sz9w7zeBzAcVnMnXPhEKoEJ58MgqNTz/FzvXAA63Zd8NAA9XN3VzFMHDGb9w4bOTf+hYqWuvX46jsuHHolufG7bejge/z4fsXF2MnTOlfuRKFXbzU1Hhf01NVhWU0eTIaCtEMfADsbKgTOeAAfO9+/XD9Nu8gdMZqMIiGVVkZzgS4GdnFxdjp5+dHzmzw2Q9a00gGC8XL4+cdajCIrplXXeVNEWhpwbyZMMEKq7YWlbevvrLq4XvvRd8wcetWXD4wcSJ+b2uzd+rcK0H9Tus2DQMN/US5z23dih1QQQEqFD5fbJsN6SgvR2MHwHJLHTsWZxTeeAPg2WfRm4XHU1WFvzc3Y1n26xe/x8fYsdgBk4FPHj6DBuH3V17B+ltejtcffhg74kTlaTiMsqe8HJWoU07p2h4U116L+dHWZiloAFgX4pl5rKjAwTaC7+swfTrAFVcA/Pa3seXH7t24cWisebhwIRq+EyZgudNSptZWrDs0AErpDASwXsybh4NHq1Zhexg+HPP63/9Gw82Jiy7CQb5//hPd590GA84/H42q4cPR20x1ez3jDByo3r0bP3wDNVUOkbwKBnHgq7Q0vll0ktk/+1ls7v7qqRxHHOFt7XdFhQlz5nwIb7wR+8Lc6mpsBzQ7a5rojs7fu6oK87GpCfvtYDByb53qavRUmTYN08vlZldmOwEAfv979DCbNAmN/Rtv7PrSEoLSFAxiO6WZSi7jucHj92Ofd+yxqLN89BHWkwMPxPaQk4N1sbYW+3insq+sxDoWDHqfHT3zTMznnTutvoB0pT598K9XGda7t/VOXozUigrsLxobccD1ttsi+4aJE73trQQQvU2cdx7m8e7d0ffNIU44AdePGwYaoSSL6D0PPhg9GH/7WzTuCNrf6ZBDrLLmZUK6LW2Wyz0RAwGs9zfc4C2NtDcQQKQ3INU3Wqro86GLPtXRDRvsupphYHubOBFl5T/+AfD6697SoaZp9Gjr3QAiJ+n8fpzMOe00HOR95RWsLy+/HD38009H/XDQIJQ1zzyDg7e1tdgfhELYFhYvdm/XxcU40RdNDwiHcTmNl8HAlhacxb/oIhzk4m2gTx807jP5RBQx8LMUvx9gyhQTcnOtkT6u/FRUoDtZU5O9AwOwZuP5dX5MhGqs5+XZZ/C50cMVKtWII6HZ1IQNvaDAcoMCwMZ+112xvzuNuNHAw7hx2OgHDEDjLdoa75YWvG/o0EhPhfZ2b6Pdbng9cuO441BRNE3MC6+uuWTccwVWN/vMf6Pdg3kdOOMMGtnU8+1vW7Pj+fn233h5q/VFVaxzc+33hULW5kf33ON9MOTuu3HgpazMGpTiI/Je1/Zv2xZpCOjqMu+U+UyPz5ecEwpuvRXrns/XdXfVM86w9gwAwPSTp8LYsWisnn46KrH/+Q8qltOmYVm1tuIzhoEzWrGuBSRlke+yHQzalT7uCQSAyl5DAyoGK1Z4VySduOQSrGehEHbgbW2W90k8bN+OecblJg2ijRiBbTeW0zjOPtt++oLq5TJlChr6n3yCRsYHH3jbT4HvIxKNsjI0Kg49FAc4udzgHgWFhdaMbU6OtW8FAP4/frw1YPHVV6hI//znaLzzelNZiQrg1KkY3tSpqLwuWYJrilVPrBNPxIFPvx/zYsQIzIf33kPlcfx4+6aX6nIbp+U3tLTnpJNi39ytd29sP3v34kDxDTfQ0bXR+ec/7d+HDwf4zneib0h2zjntMe32vmABDjKXlGAf8957eN3nw3o1YADOkn3wARpzI0fiIAsNcs+ahQMwb7yBRl7v3pj/tG49GMR1vKr8HzMmvk27aIC+Tx9MX0cHGvldwTCwvjU1WYZ5MGj/nYwtfo0PXI8bh3KR+k7+7tdfj/XTifPOs55RB3acqK5GQywUwrbADTDC6/I2Phjt96Osv/Za5wGpM8/E+Pbvx/bb3IwTHTt3YljNzTiwM3o06myR/YERGShEzuhWVGD9POMMlJdffmnpB9EYMwbTSP0YlR9tiAeAMmXBAlx3/eWXKLP797fyTZWzdI2W7tF30ndNEwcOLr0Ul/ZE6wfPOw/Ljx+jq+oSOTn2fX7UtKjfc3JwImfCBPQeueGG2DbiPfdc690MA3W59nZLR+f6WiCA8q13b4z3P/9xb89VVdi/7t+P/weDlrt7cbG1lGfGDMt71y3viosxvW7n0i9a5P0oYGoHkyYBzJ7dBq++uhIADoLJkwMwZUrmztwTYuBnOYaBCtqQIagIbtiADW34cBRGqhEIYF/zo1OE6H6id2/LfYgLLBKC/H61k1SNJz6b++absRkOlZXYSc2dizv487RQ3F5HON96C4Wmmu41a7q+5tirgtPUhPnS3o5/yb102zb3mYGmJuwc1bTrjA/6vbwc3f/IVcznQ+X5mmtwtF5nnIwaZe25oB4FRx0sj5NQO62CAnv9qqhABbm5GUfHV68GePBBgMcec8+vL7/EGeGKCmsWNScHO/y2NmuWIxoVFdg+9u3DgR4+0k6b6JGXAHkI8DwwjOSckbt5M+4FQTvrRjMadcpaOIwd4MEH2wd8aEaWnqFyHTcO48nLQ0WoVy/MS/7cokW4lMArZLy61Qm+tAMA0zNgACoEI0fi+99zj70t0qwLXwOtKpClpdasbmEhurYCoCHZlU2LKivt8o/HmZODM0SXXuqsQBO0h8T48faZW1WOAqCyNGEC5kthoTcDv3dvb+9zxhlotNFgmU7Ra23FNpCXh3tF0LtyWdDYiAYkD8PvxzpMm0a+8gr2TwcfjAojLQOjAZ4f/AA9sTZsQNlXUYHPfv65dX75kCH4Gw1OhUJoGJAhpCrLOsOeD1oAoNJXXR3pGeCEYaBRP2AA5sesWWgY/fnPOHARTelW1+5//jnONv7whxiG+ntxMQ7ANjaasGMHwOjRZlQD2ufDOjZ9OuZdfr7dO4QGacaNQ3lZW2tfogSA+fvhh5Y3UVGR3VuL+nN+DQDr65lnxrZZGrFvn73uFBbigKcXDwunNnfbbWiQrl9vL3eeF+pgOBmMdPwp91Lk/x91FHodHX+8Pc6yMpSX06dbHjAjRkSXPZWV2JbUtKl964QJ0QdRKisxTq4DTJ6M9fz22+0DUuXl1qAbxU1l3NhotXXSGZubraUmb78dvc/u6ECX+kAA+5fBg3FwnLfLxkb3/DEMlOsjR9plDO2Loy4Xob0K9u3D99u9G+t6cTG2OTVPyZ3+66/xd3p/2vjZMLD/OOwwHPRatgzfnw/yV1Sgh8G0aZFlyHWlxkbclJLeQTXoVYOT11EylEMhXHITjZISHNg55BB7PKWlqH9RmrjOw/uA3Nzo7XnRImvJjgp/R58PPYl//nOsg7z+VlaiUX/QQZiW5mZc2nvRRXZPjIoK3CRxzBisV9HaQW0tTlDQe4XDuOlzba0JBx4YPf8yATHwsxjeKAMBy209NxeVDVWp5ga+OlvDf3cy2CgeAEs5U130uZDgM/6qggAQmyFdUoJK0JAh3tPthq7zBEjMkSG0dtXNHamiAjthnm/kDuY2e3HuuZZrGoBeiVWVFqKoKFIRnj4dO41//hPLo7LS6jyontCmihynMlfvAdCnKRy2hP+UKd7Xeu/caR9dJ3e3tjas/7W17rOYNLucl4cfKifKf15m9Lth2JcoGEZ8M9te2L7dbli7deTXXov7AtBu+xMn4qCV6kJOyhDt1kvvSwoMlU1Bgb5uTZ+OhsZPfmKPXzc7c845qCxGa4uqYgNgjcobBtaJxkZsj++/j4b6gQeiXHvhBTSGhg9HpW/FCvxeWYkDFOEwhl9QYMVDR2LqByrIOtAnuLwc4960yZot4vlLitc116D7M/dIqaxEF0EaRBk+HD0Vdu2ynxyia19cRo0Y4c3N9phjcA2xW70sL8dB0l277LPfXJ7m5KAS2KsXppvKko4ao/cPBq0jJNVw0MsM33PoUGxD//mP9axaB+mIylDI6i/a2/E5PtvEvT+oT6K80i01c/oEg1i3hw7FQYpvvsH4cnPx1BGe1+XlOGtLs1VUB/x+NJ5+9jMc4IkFSuszz+BJBWvW4BKtoiKs+5MmYZmSPMvJie511dGBhjHto0AeF1SuvA/necF1grIyHNDPybHeke6nv6rhQuE0N2N5/fKXsXm01NdbcqqtDWXI7t3Rl3CccAL2XbzNVVWhtxe53upkEX8Hgt6fjtci/UYdvAJAmaueIV5cjMbytGnWe9Az551nzUrqBiMWLbLym8enyslgMHoduOACuxs4vdv06fjb3/4G8K9/Wcv1KE5+L+UR/0vtLScH5ZHXvQXKylBOV1VhvqqTD7m5mD+6ZT1030kn2b0Wqa4Eg1a/Rtcp76hvIi8kvjGzauAHg/jZtMnKC/Ja5AMHY8eijn3JJdhuOzqwbxo82BqM4u2L4le9BJz0JlVP4nWX7h85MrpxW1QE8P/+n+WZpbZhXsY6XZ88B5qb8ZruOOwf/Qjr1DffROoCqm5IedLcjHrK6tXYZhsarImuvXut8ps/Hw3+++/HepybiwMV+fmoA/r9OJHgtqfI4sWRnq7Zhhj4WYzOyKLPV19hI+Dr3blipI7W6QSJGhc31Lk7k2pccuHhZODztUde2LEDR4zHjLGuceWC4ps4EWdFojFmjL4DjXcdoUq0NYQ0qs2NewCc5frNb9CY4gpSVRWuI542za5oANjzlf7PzY0UbLye8HKjs+zb2uyu+NQxhUJYXhw1DI6ap3SP2rGTkuzzeZ8RLyuzZlbobOGODuzQ9u/HTfbcXLFpgESF8oFGoqmDy8+PVMi4AR7LzLYX+IzqIYdgHA8/bK8LlZVoSB96KI7G9+qF95eU6JVhviswVzpoV2m1/RO8fcyaZRn4fj8aNJMmYUe9das1Wk6u+KoM0dURtU7RffR8Tg7OWNXU4EwrKc0jRmD+NzTgM2PHWrLuww+t8Hm5FRSgEf7Tn6JnADeAi4r2w65dzH9X4fTT7R4JFDY/MjEQwDyaPx/gf/4HFcWqKizD9nZ0iSaDWid3aAkFLwu/H/No61b832ktKOXZmWdiW124ENfwO91HyzcoDu6ZQu/Ij8Dkz9IAEF/DypdaUJhqf8LDp3Lx+1FOqbKS19W2NmvzKwqLy0weHynlap9IYanyis9OjhmD7/Txx2hsDhyILuzbtmE5DhxonaqivkdhoTXAc/vtds+MkhL35TymiQb8229j+GVlONvoZFhPnoyK61VXufcx99yDg1JkpHJjh65R/MGgVfcoHhoM3rMncjkT1XfdoL3Ph3kxbRoOvG3ahDuku+VBRQUOBJFXnmHgDKnbQHc4jDN8RxyBxu6996KBUFeHZTlhQuQzXAZRPOTJxN8vNxf7Eqrr9H4kl/71L9zIUu0Xdu7EJWS9euFsJa9r06bhBMX559tn0MvK0FCZOdM6vk0dNOHpLijAmWSfD/OV9wtlZWh4zpiB9ZbXfz4xQ4NuO3daM+A6XUrNt8JCu8zzuoyMvLSojvB8I12SlpnecovdkKyqQiOOLzmgsPgyTd2gCNV5VY6pfcOrr+JyRBoYp7zgujOPlwz1piZs3yUlWHa8bajtgi9NJdTZ+sJC+zIlXVrpOdoDxIlTTrGWelBe5eVZXkJ8QFbNG8o3SvOUKdjnfvEFyqrRo7G/p7Ry7x8Kh/5SGfH4aIBo4EAsX5JPPp99sIb00l69sO/k5QGAdebGG3Ewkcvc8nLUm448Er3HdLpNtiAGfhajCjS6RooTjXxxqqtxdJ67B5EBwAWiboaNnjFN7EQ/+MBq5LzD5J09F4gkPMvL0ZV09OjYZkELC8F2VrVqrPp82LGrglIlHEajYPfuyEGOpqb4dkh2Qh3ZrKxE5WzoULtCP3o0bmhiGKi0DB+OM5X79mHnNmmS5ZbPOxH+4flO66zo3eg6F7RcMFMY3P2W3r+yEtPA9xbgioWTAFXDVv/nSlNjozfXw2HDrDWR1dVo0G7Zgh1FXR3m41VXoYKrGsW0SZTTKHlpqd2FjzaHeest/TtNn46d7N13R+4kbpr2GY7ycixLt1mP2lq7myYA1ucFC6xzdWtrLYOWp4m3AXUWUzW4+G/Fxbi2VgflUyhkn/3w+zFfcnMxv9vb8Z1pZpMrgWq+EbQ3CEdNH6/P/fqhgqGTefR+xcWR70oUF+O1qVNRmV6xApXg4uI2GDLkGfj66zlw0UUBWzmWl6PyPXGilY/cC4Knpa4O8+CLL1ApGTwY86yy0lI+VCNTzR/VAAHAwY3//hd/mzIFleqbbrLLzJoaTOdBB+H35mZU9O+7L3KQ8Pjj7R4WwSCmUd3UUScf+DvwdcWqgTdsmLVUSvee6iwlHVfJ83XAADQOydBS88/vxwEecveltKpGiJp2tV3w9HPDl5TL9vbI3c3VdyouxvQecgi21eefR1feyZNxyY2bIk4UFlqDibo6zuPt1Sv6APKXX+KgwfTp9sEjdVmdYaCy/e9/28t7+HBcMvHhh+havXmzPe9yc/WGjM+H4e3fj599+zB/3DyRzjoL35/6no4OHChxIxjE/AXA5xobsf2RZxhHd/Qvpbu4OHKPGJ73lCehkHV++G9/664b3HYbyhi13s2di0tR7rgD83bgQGuvBG5cqXKC07cvvvvUqWjor1+PSzN790bZ0NSEsujll+3Gm255gvoZPhwHWZwGdEpL0fjm97stIzMM7KPHjMG2TJ6l6gbANIDX3IztaPVqlHEAOKA5dSpeU9PP06nqnKq84m1p1SpcT0/MnYuDarfdZu3LRIaoGif9xusrxUueTnSUId93hssW3u44gwdHDn7o+lDqx37yE5wM4n1BVRXqOepyj6Ii1B3oXi771Dyk/8mjpKMD32XsWPSgHTrU3herBr5aRoGAfRCZUJf4UP3n8p4GGXw+rEutrXYd6txz0Yvn3ntRRvfti/dVVETaB9mIGPhZjk5Q0+wcjYqp9wJECkFyu1SVIv6sqmgB2Bspxa0qEKQ8qRuLkOua247LHL6+lMfJO8NgENc0uikIP/6x3TWVh+n3R9/hvagIBwe8DAB0dKDyXVKCHV11NQpammlUhSR9cnLQeCoutmbOeL7n5FiKLA+Hd3z8Os8z/j8vR+7SB4Buq/X1GPdf/oKzD8TTT6MSf9FFlgHE4+R/qXzUzosL9pyc6C5XdEQTV1K5uz4ZYFOm4Gzq8uWoWAwYgAYprTtT00ppUcuA6pNO0aZ0NzfjTPqrr6KiM3EidvR796IisX8/GunhMC5DcHOtXLxY7xJKdWH/fgxH9UBQZy/ICOXX+P/q7KBbm+fGD0Fygrd5+qjGr5pnPM06uGzg9/BZGj5QRffSOmK1DHWEQpifhgGwb58J7e0ARx1lwqBBaOj897/W5kx5eZEGglpHKI90catyhstLbqS99RYaxtHyZPp0nOldvx5lyIABAEcfjWkml2AAHBD83vewjW7YgPJnzhx8jtcHNX28neoMfJ2ypLZlpzKg/3ftwvTQfX6/tQaboPKmNfg8fipnvpyE7snPtwZueT6r5cXfmaAlOjTIaBhokNDRo6ocVTeNpLRNmoR9xKRJ0XfIJ3r3jpTNTvnsded/OoedZDu9o5NOwPcg4Usg1Nk+w7Dvq6L26RQOXZ82DWe8b7nFboyUluKa2gkT8JlQCNP59tvRB/3pNJRJk+z9p06u6GSB2v/x96PfeZ7QxmlvvBF9Gd+WLbjcorExss75/Sh7Bg1CY4wMJTVNXNaprs9kEOfkoCzo3x/L47PP7O1PncRR35+/s6ob6PoF9Rg5+94jJphmpMC96irLG6iw0BqQ4/DND19/HeAPf7C+n3giDoLMn4/7HvABZC5nVF3Q57OWqfJyffZZ/QlJGzfiUa233IKeL7q6wPNU3QCRDHz6jbxCSCbzGXzeVtQy0clLXkb8M2MGHi+5di1O2h1wANarHTusze14H83DVtNC//P8JR2TNjLVySddO+LhUV6om+xu344ykstSVS53dNjrHJdjqrwZORLbQFGRJXNVXSobcVCjhGxAVVrof75juioU6Dne4FWF2WnESzc7pnbuutFT7gbq81muXYaBBtJPfxop7FSqq63dOQFwxFUVXvR9/Hh03VF3nK2txc07Zs6MbPj8/+nTcWRU3bCqvBwVlXPPdU+rSmkpCuODD45UfouK7K5uhmFf+82vq4qCmnbdMxy1U1TDePVV3M2cuPNOHCz5xS+w81N3i96yBWcK1fXzurqjKl+0PppmLwCwLvz855GnCVRWoiI4bZol0CkscmlX3SwDATS2DzrIWuPFFSc1X3WdOW8f6nvQ39JSNCqamrDe0M6sdJzSIYfgTFNODo6633hj5K7KlZU4sDFnTmSHqSp7TsqBbrCL54Wa//x/p1lvng6+iU57u92Vjis+TrLDC2q6eR7wsnCTV2rZqOGr78vLm3YQnjoV/6q7s3PF2U1p0IWtKu4vvIAzLcSiRajE/vWv+rBJrgNY6Zw2zfLm4EonvSMA/j59Os4q5efbvRwAImfT+G88PJ7XKmp9VduSajjQOcdO/ROlX3Xv5L/xMuFpUE/70Cl4XP7Rd55+vsyFBh50z+tQld7Ro92XfRkGzjpNmRK5H4Jh2GUh7S3hdRkZncPOByh0G+MCYN+oGgFO+xn4fDgbW1Ojb6tqXTAMlIOPPorG1cUX4wD87bdjX1xWZqUzlgEMuk9tm06u4056C/9dbT/0lwZ6vZ4RvnVrZF3gcdC+NjodRCfHObx+1Nbqn6e0G4bVR6rvqdYJ/iwt8dPlES/fGTPwmMPaWvt9lZVoqC9YYJ/F1ckGuv7CC1g/tm+3h7VpE15/8UV7ulUdVs0btU9ob3feMI6MT1oCRbKElr+p5cL/0v20lh9Af2qRrt25weWT+pfePxTC5SAzZqCuw08V4u9fUaGv36o819UL/gGI7EN0ej+FTbpQba1dntTUWM9wQx/A0uUKC1E/pLxV80ONU/duYuALGYuTclRX526MU8NTjwXhYeoUOR4vgH0mEMCukKmdFP0/YoTlZk/Xm5txYyi3uC67zB4XKT46oWsYKPCeew4F9qJFuAP1hg2oRI8dGykEeVx+PxpbH3+MCvfFF6P71h/+gIrKwQejS1AsipbOSAPA0ff+/a34SbAOGxZZFpSvpFjxZ5yUAVWB4Ongs7L/+hduIqW6223diu7uTt4KhoF5ox6zqP5V352MFHJr43XhiSfQhW7hQlxT//jj1mYv5JpL4eTn240xng/5+ZFGLe+odGnTlZNaR+jTvz+OHPfvH2ncqp0/fZ8+HRXdu+7C2Y877sBjwuical2ecaNW12HpDCWnwQA++66+kwoZWUuXYr0nOjrQOF2+3Aqrf/9ImaPmWzSPF/6cbgCL56taruos4wEH2Jfz8PCHD49sL1xp0BnLTvmra9M6ecTT+dxz6LKsbpi3dSsea/bcc/r6xzdoUsuM5xn9rxpoFRX29saf9fks7xj1vXmcuoFYta6raVTzmW+Yx2URv8YNfDUf6TcAfI6Mep3C5/ThaVXbCqVPFw5/X93eHGqeBAK4XlYHhUfHvqqDVlVV1tISAKuNNTVZpzo4Qeew+3x2z5BBg6y08+draiLTpb4T/33UKCttqrKv5hddC4XQi625GWU2GR4jR+J9Q4e6G+gqfKKA6sqAAeh1pqZX9w5qGfbvH9nmeB9pGJhGL9Dgik4+AOA1GtDg9dEwMI/UdqSm3TBw0oMGR3ia1Xi5d4gahs9n98ag3wYOdJbj/Nn6elzv/cEHbbB48ctw1lltcNddqG+dfDKG3aePdb+6Fp3Cam/HdqCDyui22+zeDOoApK5983x46SX3I3lNEwcT3nwT6+eIEZjHPFyuy6l5FghY9YPWltM9usFSt/bLf3fqqw0D08knDAYMsD9rGDjQTXumqGE6yUb6TTfIGgph/+rzWcv0dM/y/qK42Lqu1v2hQ+3PDx+Of8vLrZM8SD6qm9vq0q0O9ujaX7YgBn4WozZ2APtol6oI8+e4EsMbsK7BqNd1jYl39NzIB7Bfdxo4+O53cddPdfa2pgbdwGfN0j+nxqUasKNH44zcQQfpZ/10o6p89H7yZGumjHf23/42usiS4NJhGCh8R4+2vjvlL/9d7UB4Wt2UWKcBCzW/eKcDgJ3rPfc4v4cbpomDAq+84hwvxenkpaHWY3I9nDoV3eXoSCydga4Li8en5o+TsI+mKLilmX9X65MuXtrUixRdpxllnnfqLItqfDnVZ17OujrO30/Nl0AAB37U42oA0Di95BIsdx63k3u2V9R30qVLVTj4vfRuTnG7KTNqXaHrXB7oBoh0xrguHvp7/fXueXDzzfodibmBz39T00lpUpdcGYZ9vbtax2knaV5PdfVRRc0vXV7wvxSHWmb8u64Po790nQaYVc8Kng6dIus0GEHrP6PVZzf5DRC5yeSUKeihoxquvXvjxmu027s6aKQzymg29KKLIuPnaVi4UD9QwcPVtQG3MNX8V8Pl9/D7eP3V3Ufw/VhUPUClshIHGSgNan1Ww1Xfj+7VrXnm+aTWqcmTLYPVLW20x4iuz1KvO8lxtd2oz/M0A0TqDTz/1fiJYNDqW51kFn+Wp5vLYb8fjyCbPNmM0JV4Onkd4Hnt5cjkLVtwiQQ9p8on3Tvz93Ez7jncg8Cpr1DrLx+QpO88fbSenYcZDacZfJ4OHTpZp8arDog7/c/fR41DrQu6dOnqAd/AVddfqnCbRr1XJ5O5/uekQ2QLYuBnOWoDJMGn24RNFexDhlgbUvTrh6O2FGa0zh4AR29p5J8aFKWFGldurjXb4KRQ0N+5c3EN7K9/jeu67roLN1ehdXZOadF1GIZhX7+pe141BAwDZ7iqqiI9BHj49EwoFF3ROvNMKy8KCjCvq6v1+UnvonbwAJYyaxiRszeqMNf9RmGohsvAgbhesKvHvXEDcP9+expUw1RFVaoqKlAZLijA+sPXtvFjn6KhjppT+DTKraZNl4ehEOY3Lddw61ycFCO1E9TNCKttQR0U0bXnIUPs6VYVNR53fb1lFNF1et5NOb7zTvc8vvNODDMnx5q10uGlg1XzTtd583oCgGVZWoryi+jVy34EnRoHLxeeroYGaxd2nbLTp09kGE5yiT58dswwcA2827ps08Tf33or8v3Ly+2us2o71+1kz3+n/OJpVwcBwmEsy7o66+QC/jztMK+i64fU/KE00ukJajk4bRyqeobQLG9REaaRyl4d4FBlpa5cedpIxvJ0kfxRy7mmxjrFAQDTUlGB8fGduCndzc3olXTXXbgu+eabcZNaMu55/vh8GI7OyKUwTzkFBwfUe6qr0dtt+nR9m+Zx6FD7HJ1s0skXnw/bIclJ6vepTPv3x3apylsOze7SCSVqejj8NBSSP24yRtfPOg1W0e+8TdFvgQDOJOvCpO+LFkVOdtAsKw9f59qs5iuAvZ7RfVyWq8/yvtZJrtO7cF2R/8Y9SnTvSffoBqF0sonuVweb6TevyzL4Jn+6vWgKC60d97k+ahh2LxU3+FHMTmXDr9fXo2yuqbEGYSsrrfpcVYXtUj1qN1qf2LcvbrznNAikC4v+V72k1PfR9YGq3CXdh9qkmh/qYKiunuTl2fsLw8D3UvuoAQOc+4x4DHz+bmraog0eZhJi4GcxTp0vNU61E1OVm8JCbDj9+mGlp9loXeenE0ihkF3pVde+0KeuLvpIH/3fuzfO3M6YgbOb9B5ueaAuCaDr+/dHV/RVAUHp1S1Z0AkVWjeurqvu0wfXpvEdq30+zGv1CCo1D1TB5PNZGyb26+dsIDp1rPwaDzMUwjKPtmmQF/igBSkvHCpDJ+Ocv2+/flYY9fVYTym8UMi5THlYXLni4QPYj2fj9/LvPN/q6uwbAdH7qPmqMwrVukNx6NLLf1M7K74ezjDQoOBLXQDQpVCNk/6S0sPjJIXDSdl46SVvsypr1+LzDQ1W2anhvfWWfRmHDv7OXFao+cPLtaQEjQc+IEdLJpzicGorpaWRLq9cvlRX6+uJTgHj/9M6Qp/P/bgwjjqLBIB569TeDQPrKf2vejjQfeRiqsph+l5Whs+GQqiIqfdUVOi9lnT5ov4GEGkw8XucXG7V+hQIoFwwDEwjyQPdQBWtPeXx6RRBkrH8+Df66AaJe/eOPFe7Xz9rs1pdH+r3o0fSoYeiV5cqC03TGkygsFSoXEtLcXDg8cfR++rSS9ED7pVX7Eep6sqZZKiuzfP8of/VfNN5DwHg4Bitxab6SPlZVma1A51MJMjt/5BDcG8e3druq6+23hEA9wfxoieo7+VmJBM678C5c3FwRd2jp3dvXONNy5m4/FD3A+IGPr+mxgUQ6flB4aqeCXRNlVu8jPjzVL+4uzR9gkH7LvE8jXSkrZue6HSdDEb1nWNdlqEOkNCH60eqTJoyxd37guTJ0UdHppv+5+9Nv+XnozwiwxUA00Dy2u/HtuAkh5yoqsJ+Xk2L7nmnvl19P1X26doFv079uZOurIbHyc1FnUTtL2pqIsuI9svR5QnVVZ5milPND1Umq2nncWUDHue6hEyFhLoqwAsLcbSTd2JOSpcuTDfh49QIaY2MKgB5mKpwjxYHjSTrjgXiDZh/eDx8920VnQKki0NVSPjH50Mjf8oUfPfnnkMl+txz0XBevz5SMOvek+eJeq/6fvw5w7DvFuuWl1RGtEsq1Qt1cCIWqNOmI4vU9wCwlFLupqZLH/+f1yPuvZCTo1c81LgB7Mo6T4cat1O5ur2zrk7p6ju/nxsU/B7d6RVqOvhGeW7xqvVU18HxNFC+8B3YCa8ujdu2RSrKzz9vX7Zx8cV4Xu1dd9lnLVV4uvnGnPw3kglu5ekWvmpk6H7n8fKZJ51i5KTM6sL2Onvg5g2hxq9bOqAaYepmevy7qig71X03+cLbqs7wcFMK6ZqTQavKPKeZQ1Vp5OnmcfIz4Xn6SM7QrvtqvnhFddHnaXcLzzT1hiq/V63zfj/AuHE4uDZ6NG4Cp9s0jcfB3bJV3OSEKqPodxp8dupjaQCGn+rjJl8pjilT0Bvh97/HgbGyMlzapG58SDP4ra3ue8Wo5aEa+Op7kreYTqbPnYu7uv/jHwArV2LafvhD9EDk7+dkHDm1B/6c04ComnZdu1Vlv65O8Q3hdDIrGLT3C7rlK04yWP2uftR0jx7tfuQeABq8TU3WiSnR+l914sfnQ++L737Xau9qmmk/DKcweXhe9AQ1HjVMLzjp0row6Dp5junqHoBVdm5LVNzkDq9fatix9Fs6Galrp1Tf3O5za1tOz2U6UcSokMnoGhXvnEjIqc8MG+a+WUw8xr9hoCsOX8foJFwISoOboCIFwalRkmKoa9hDhthn5FScBLnT++oEBn1yc3FWYeZMa9d2p46NP+8UH+UfzVZVVOiVXToOMdo7AOCgD21Qx+9bsMCb4eGU/htuiBwx5sJ7yBCrzg0aFP2dASxlkBQZnw+f5e5zXtIZbQBHd93ns7vx6+7XeSg4lbeqZDnlo1qH6VnVBVX3PN0/aFDkfap7HW//bh2zV5dGdYBo+XKcUaRzo4nNm1G5+stf9OGo+TV4sLUDuKpIGAZu9MNRNw5zioPLFkKVBer93ENFDUd9xun3nBzc8TjaLFJVlbW+mNKm1iceR2kpygh1Zs1J9vK0mSY+V1+P4ZAipabPS5/Rty96T+h2B6e/vJ0MHYobKlG5hcP6dKoKrm6JAKVBjVd9dy4PSB7RPbzv4uHRUq9oA4uEzhOAcNrMjggELE8MHUOG2M/PVt+Xywp1kAcAZxsHDLB2X9cZOfRXN1ADYG8rvK7p3mfgQKu81IEPpz6Rt3fajHXWLGvjM7Vca2osg8YJnYyPNsDBl7Po0uv34yDE7Nk48MAN5WHDUH6p5cDjp7+GEemNRc96eR8uD/gRlzzNgwdb9YauUXr79bNcpJ3CBUC51dBg36ndaTBK1475/2pd8vutZRlO78qXPgwdimnhG0jytHCXbl6OTt4XffrY98NQ064OxLjJfvVZt0EnL+jkmO47x+/Hd9S1r9JSzLvGRrtngW7wlf7XtXG3yRMneaD2Bao+qD4TCFiegbm5zmHp4lIH8r3mdyYhBn4WwztCtcMg1IYKgG4sOhceHq7bdaeGW1KCyhDfYIQLDLUz4J2yU5iBADZst3XXfKSdN+ZQKFJp5GFHm0VQ79WNINJvTmsAdR2E+rvTvcGg5d4IoA+Drw/1MiPDwyLy8507Vwrj4oudO8UTT4x8hgv8/HxMZzCod+/VdTB81p5G7J2eV+HvxpVs3WwD3a8qntyNX5dW3eizOgPjNIvjNFDEw1cNfFq/qqsD/HlyceX3qbPPeXn2Head6qYXl8aaGlRuiY4OPPlAByk6Cxe6z07Rp7bWUtR0xrhuqUa0jpyHrxoq6vNu5cLD0c2c8d/pvYNBHEhzWsNLXHJJpCu7Wj/V59WjNnWu8Lr6R3/5juQ6OaLWGRWqm9zVUvd+vN0UFlpKm89nDVCoSqAqd53aptMMDy8L/iksjDxVQHc0VEcHll20pUEE7wMJioOOhNLlj2niM7TjNcHLo6jIng6d7KeBCCcZQ8emeVF4dfVc1S/c2lxJiX2tvK7uucWptid+5B/h91uDUzpjKp5N9gwD85HrKE76gppGACyjoiJ7HeL3q8+RazH9RkciusXntrxAHdTV1Rs+EKS2PTWd9P68fTsNXOjqg5q3/H76/ZBD8HQR3UbLl1+Oyza57KAjanVhc89Nejdi7lw8Iel//gcHop98EuCjj/SeZbo0O72LDiedzEvbU8Nwkv+6PquszGorurhpKRZfusfro2o3qHGoy5C8TKSov/l8+j5Ffb6szD6J4FQmunBUIz9a+jINMfB7CDohwGdjYhEqToYQhevUmAAs5Ua3gYxXg1pNfyjkPCBBxjV1ok6zpWqai4r0o466dFB4OoXN58P08Q7aS2fglo/0CYUsZZaMffWoLNp0S81vL+/EOeQQgB//OLJzrajA4wFvvBE7xWeeAbjiCoD773fuFLlx7gWeHioTPoOfl+fsDqiDyp5m0vh1vkaLrtGHGxvR0hrtiB5VQY3WGapx8vrLPTSojNXzvtU00vPhsN3wcepQdXXC749ujF57rXXmLwDAihXubpamCfDpp7i+3ykdPC94PeDvFm8nzcPRDRrqZIfueTU9Otmg1gfyRHKaRaqsBHjsMfQCUtsE5W80g0M3e6u+i5sspryOdqSh7jkVXTp1dY3K2GkWyK08OE5rb/l3J5dinw/7BJ2BT+dbq/twOEEDkrq+V6eUq++g5qXbe6vvmpeHdYDvW6JDF7+ur1Tvyc+PHBjzqjiryw9CIXQB16XN7a9uoJPex+tsqVMb0NVVt3B4WLp8oPzSlan6HA0I8Ht0MorqKPVXPF/UGXw3A1M3KcHboVu7o7bkpEM5GXxqe1SNx0mTAP73fwFuuQX1kSeewCUQfKNlp3rGr/v97hsv+v0AEyei3jN5snNb0b2PWtZu6dHVyVj7LnXA2E13VMNX79XVZ93v0a5RPSE9xGnJBEed0PPavlR0cj3aJ9oeHZmKrMHPYngDGTkS4JNPcA0eF0oNDQDvvBN5f7Rwna5FE66k3Og64WhCVL02ZAh2Zo2Nke45BBn46vr/aCOKDQ125cJNaJLiRC7HqpDy+513XnWKX5c2VXA1NlpKJd9Vnj8XCKCbWk4OuruuXWsP362DV2cAJk/GdYWvvQbw+efWyQqNjXiP34+bBgaDOLvqpkDGOphD70OuybweVVfjKLzX3fMJ2nyJx6O6+I8ciXG1t2PefvSRtzbitn6NfyclaMQI64ifYcMA/vMfezmodWXUKEz/ypX2jZP698dOlRsbTp30qFGYf1TPGxudjy9yqv9kjJ5/PsBnn1nXy8vR+D/+eJQ5VC/ddojn6Nb369KhzhRQnnpR8tzQHR2ohqEbSBg5Eq+1teF6W8PAdkf1jKdfVQQHD7YGmGgN75IlWBdqanDJwaRJuG8Hbz/cWKTd7det06d9+HD7gNCbb0bm66hRALt26QdiKH91e5644WaUUtzUpmkPEF0Yavl7NSBJbqptEADLjNreyJEAq1bZZ3Up7YMGYZnyAWoAlHVuM6oqfJBbzQP+XUXdxd/tXmLUKNxrhzZvrKpCL55gEOWHW/xugzK65wwD+wMu94YNszbZjNb2evXC9G3din1ZQYG7ga8zbJzSGi1+p3qlW6LA/3dqz2rYOhkOgIN24bB+cIjXWQDMW54fI0c6D+INH27XBdQ+feRIa28VHdTOVeOTx+nWLxgGDlB61en4wKiap+ozfj96hbW14dr8/fvxemWlvn/R1ZERIwBeflmfNrd0OoXtNmAR7dlo+w55TaMXvTKanInW39XW4vHPOrnLw1brIPWLbvH062dfohTLJJAuveoGnE59CgDWpw8/dL4vUxEDP8uhCqxuCEPozk72GmY8kJKiHrHjFK6bok6uhk7GvWHYXXhJceRC1amD8vkiO10n4Uej5eq6Nh5OtDW8boqTGhddV11CeT7Q836/dZ0foeWGTrmjeGnTpq+/Ru+BXbuip1uHri5GSxOVJ4C9/hiGcx3QwWfwQyG7waPbpIpG+9XnndJJz3E3c3VAi+cnXz4CoF9LprYXboDyo+10rsJOnTqFQfXcaQYy2ow4GaO//z0OIlZWojE0bVrk+6i7RTvhtL5ffRc+0MN/d5Mb0ZQuAHfF1E1RoXrC67euPHXKkeo94vcDHHQQrl3PzcWBEh18Vs0wrPLXvYPTrBp/D1pP7iQXY53Bp7rNn6Hy4oYPDdaoyzOcdp1XyzqaPFFn8OkZvjZadV/nZcjbrvrx6p4PYNVZHdHqp87Ad5NHurX+VB/d0szLRL3Gf1P7bzV95D3n1auG8plm1Jw2z6W/vDzV8lHjc6u3Tn2tlw3QVNmjQuE4/a7ru/i78f+5jHbb80HVBXg6AKxwnDxWnNqUGqfbOzu9lxquGh//TTXy1LLmf3VLT3gcvF66HY+sPhetv1DvUeuDm6x0arux6ka6tDjlqdP9dM1tUIufSMLfQbfEhJc/rzdOfTQP3yltXlHDcnqe3sNteVkmk4VOCQJBnQNfY6S6P3G8CDxan6fDTbmma+Xl9jW/FBZfi5dIfD7rLNe2Nv3sfTQhzO9z+g1A3+EaRuT6V52ANQznQQBdXNGg+3Su4mp6vMTj9N2trN3oqjtUcbF9SUIs+P1Y38rK7EqBlzTRMWLRoL0hACzXa92HKzA+n/08ZKfOmuDGllu760pH6RSGit8PcMQR6D4+dqzz6PvMmfazx3Vx9e2L6/t1qIo7d9+mI8+iuR67wdtNr16R96szcW7huXnHcOXdLSydvKiosIddUaEfMItVVujiVeUiLV1SByPcKCjA9PJlI6FQ5MZhoZC+3pCBrxqqPB+d3oO/j8+H7T5WRZHnQ0mJfQbfq+HKofJS44jWRktL9Qa+l6VJXuq9eo/XGXxKm1MYXuSHGh7lrWrg83DUJQ5Uv/g1kqe0t4FTvdXlv8+nP4JOfcatDZPsjycfVBkRLzxPY/W8iTZwpUtfbi6WhZM+pAtX7Rfpmk5Xc4o3Wv7qyi4aXtoNX5ZSXGwN+EWLgwaz+FF3XuPl6OqWkzxx6yOoD9EdE6zey8uloCByj4hoeLkvXh0xWj8aLY6utrd0QmbwsxjDQGVbvcb/j1Xxr6/Hjy4ut3DoWr9++NmwwX6f267kOrw2Qp/POjpNPVot1rCipWPMGHTh5mdUq/lPaeLP9+uHhqNTuPxarIJfjV/97iREVaVx8GB0naRnnAzLWDrOIUPQ1d8LarglJfq89QLf9K2+3jqujV93guqvE5ROnrZgENe87twZaYzwfNa1Vbf85Uqwk0GmPsPj9orXMu3TB+CDD9yV0oICPJf7u9/VxwMQeRSR7h6Cuy337o1HQqq786vPu70L/TZ2LLp+ktseJ9qMnVNa1TTEqgzR35oagI0brd8rK/WnXHhVkJzqGf9L5OfH3u50z9Cynl277Er/2LG4TwNHPVpVl35dWnX39eqFcbz/vnWcmu5ZJyNjwABrWUN1NbbpWNtTXV3kTvhqOnRhlpdH9hMA6P7Kl8foiNWY0dVPXdroPqe0+XxYX2mJgBcoz73O4DvJTX7drd7qNtkD0N/vlAe65wFQ7+DheC0HLuO7Cg/Lq+eNYeDgiNuJDU5yrLzc2tRQxedDL64+fSLTR/+rs8P8Hp0B60UO6wzgROQtAMCBB+JSOQA08MeOBWhpiZ4utW6oafaK7t3c+nynfPP7o8t2Xpfofzo9Zc0a72mOhlPexaOru5VDthv4MoPfg4nHMIs1XLdrXkan41Gg+e80Kqm6/jh16m5hOb1XLJ4Q/P54DIREdUpucdB1L+6M8QhhtxlnJxJZP3Vhd0d46owtgLPXim6mWFceXGmLxaDzCtXXeNqJ0zNz5wLcfnukQVBVpT+KiOM0g8+v6dy5vaKm2S2caHkSi6dTrPLCS3l0RVYks71Fi0uN16uLfrQ4ol1zM2BVujKDr0tHLAZLV+JI5P1ejp2MNX66z4uB71S/E91H6uKm//l7eYk31vxKxLtw+RGLge/lnkSVgZNc4x49bnntpX6pEyte0+UlXDdvrXiI5Tl1uWI04z4WvV/N82j10ss7e80XL5tcew3fqe/k7xGrTZAJyAx+D4NXct5g6VpXBJKTwlVfHzmCT0pbLGnmz3pJZ02NJSRqa611fW6bG8VKPM/r3kfF59MfFRarkhbPPdXV6Imgg9YCO7nFxpIf6SRIk52W2lr7LBu1PTeXUQDr+Lvdu/XuxaonhZcZfDoixwsVFbjZWix1yYsROns2bgj46ad4+kJVFbrv8zO8dagGVV1d5MZKXhUWp9/5/06zUF7CqqhwXn9PbpmxyF7+e2Vl9CVNsSpEbkZ2ssjPxzxyWsYEYHmoUNmr+e+lvul+j/adX+ftih/nloh80r1DIvPfi1Gniy/atepq9JhxWwvOn4ulPlI/Hc/65erq2Jf7xVqWqpFQXY370sTyXDRKS9FTJ5aBFB3V1RjGnj2JN/ATof+obZoPWhsGyvj337c/q/YDbrLLKX3dMRBkGPEtI4wlT3ftQt3ArV/ymlduaVGNfbd7vYbpRL9+3jfk5ejails9rayMbblZpiEGfg9DbdyJVup0YZSVRa5lS1R8bnA3sL598e+ePZEKWyydmdNvsXScuvO1vcTnZHA4Ee+MQk0NGl269OTk4AkD//63ezoTkTZd/MkgkeE6pbOoCHdBV+uJk7s/dVS9e+vPtdZ991qXGhqc06+insoQDYrL6z4F48Zhemj9tdfwieJi/Kj3uD0fLXyOmwtftDrpVLaUP7qNiXToZp5InrkRq4t+tGvJgE6Q0KWJ4DP46iaBOq8YHW6ym4fl9Vkv6/690h35HK0uOL2j2w7f+fm4U7bX+OMxBN3W4DuF56Vt6OKKhpPxAIAGlnqsZbzx0D3BILo/v/129Gfc6NMH83HDhtQb+NHaIYXH00kD205lH21wjBul8SzNjFefMQz3Pau6GifR0WHfBNSt/9Xp/F77RC/l3VVdmnDq12NpP27X6X9dH90d8ri7EAO/B6G6EukaelcqdyzPkxCKVeGOJy71OSe3Ly/PeklHtMEA9b5YjklJRGca7R6vnbDu3lgNQa8kW+imo1DXtQ+1DdP/dK9TXerqbEWs+RNL246lLkQLN9q7RotLlY9u7SMeGaRrO27heDVgneKKJU1eZVN3ww18ft68rm0kytjQHXHJv7udIx4PTmuNE0W08LysRY23z431xAUilrPHu0IsS8Z0bSLWJUyx3OdVT4oG3zcnlvij3RPrMhUv9ZDnsZPx7hanzn1fF3e0tMS6BMVL3+GVWJ8NBu2nAemMWRWv76frp3T5H6sNkQydy22fFq/pS0ddMF7EwO9B9O6NM7A02qe6GdbWRo6aeYXCGDTIW8fL0+I17GjXvJCbixvGqTuFxruLvpOB74TXdUWxhusURjQaGry7KOk6jXjKJh6Xw0wRul47EK95pN5XV4fnJhMNDTiTRrP8TjvyVldHT3u0NHu9zzCwjbm5JnpVQlTq6/H8bp4HHBqR13kMRYtn2DD08OGo7TWWXfR1lJXh5n0VFXg+eTCIZVpYqDdoaJnRV1+hO7RXDjgAYP16b/c6teFkurC6oUsP9VX19VYfpeZ/fb19p/5oYarXnf7v189e34YMwSUzra2Jk0vxtod4w1cZONDZMOqO+HXx1tfrj6/y+VDPcNtMM1a8pk23dC6WfBo40NvSGgozPx/1Mt0mmrHitKdBtDREu6dfP32/4zVc3Rpyfj8Alvf27bipZXU1ykO+JIL3OzqvDwD0ZOB9UizGbTSoH47n2a7EC4DyqLUV4L33rGcNA6/z8NzyN5Y0dXXwItZ4Y3muTx/UhUzTOj2Mv3dDAy457CmIgd+D8Pvt50urDbW2tmvhG4b3AQI1LfHGF88z3IUvEUpMLBu+6DwovM7gxypYvdzrtHQi2qCH03qvRAh/t/iSQaIV2Vjudbtf9zsd+0Tw8nNrT6Wl3tPllJZY8OIm6zZI5IRup25OIBD9RA6n+PLy7INdTkZuV9yzDcOSszTokpODexDoIDn51VexxZef3zVZkcoBNZ3sofRwQ0dtH06DOvx5t2u8PvL41TaXm4ufL75IjFzi9UynhHcVLwOqun47kXXC6zvxe3RtnX4PBp0H+ZINf5dYy4tPLESLg+iqnsTDjGUG38ukh88XXSZ7QbcGn+dBOIzHHBuGJS+3b4/sQ9wGGtRBbi/LVryWrdtxivEQy/O0n44qQ/g+Ozqjnu8l4iUtPD+6uoFrsp4LBPTtxTCw73Dy/spWUjRGL6QatZPKNBKV5lgUDy9KotM1gtxMo92riy8WRSInJ7FCWO3sohmmXsJP9GBFV0hkffJ6n9u98Zw00NU0JYJUzQjEGqdXorkud1e6+RpLr8Qq27pjoM4LungTvamTrn3Fs8t2MnbRjyX+WOKIFZ9Pv0lkrMSyoa6XOJJRL72E6TRQkoyySsY7+v3ePCapTkfbHC43N3F76ah9AXc3V9OmPuMWri6MaNcTRbzhx6u7cQOfl3NXZYuTgc/3BnKKK1qYsZKI55zCiGczxHRHZvB7MKlU4mIhXQRxIjqKYcPso4hOI+W6MIcP9x7XAQeg21a8eFWy4jmuJpb7vKanKyQy7GhlFIsRlQjjgeLp6lrqeE+88Hpvdxr4XuPy+9E19IsvIsMA6F4X9oaG2Fz0AVDWeNlN3MmgTqWLvpti73afW5hujBhhX/fbnXsPGIZ17F6y2kE84Q4dGrnBZ6y708dDuhr4dJ860J3Mckskank6kZuLci+a2308MikabnvJxNs+49Xbunp6AYURK/HqbsOHY/61trqXc6wDuToDHwCXlqn3JUP36y7q6wHefTfVqUgsYuD3YOKZsXCjuxttotLstcNwis/LBkVELOu01uq8GgAAIwFJREFU1HC9jL4TOTneji9yIlreej0qTEc2r8GPpYzcSJSBkajONNGDMbp0dYdRFWte6I4T5ApPd+H3ez/akIhlbw3dLE8q25wXA193X7xxqAqx13AT5aLv91vGc7IM2FjDVde/G0b8fUqiBmJiCSsWupK+RKcnWe1Ot5+BE16WP8Qjk6IZ2zRg4uTB5jQo7HVQ3cv1eO9Tn4m3HOPV3Uj30M1E69ISj2cNf69Y9N5470vUc17C8HKKT6YhLvo9FN5YE9WhdKdiksg0x2KQqB0f7cjvtN7JjVAI73VSxFM1g0Y47SQdCOD/paX4V53lScYors+XPeeVejUQEzEzVFDQNcUcAGdyktFxp8JQ7mp85Jbaq5ezTMgk+HuopMrADwTssq+w0FkWelXK1HehOt2Vd8zPj1xyFS90BGAy2kQiZiIBMH2xGIlq/ImQIYWF1oBIIvGStmAwsnwSrUN5TUumEk3WuM3g0+9uYTtd122+6fMlV3anazlSukj/dNqYVL2f13UnD4FY9L94SNRz6Vo2iUZm8HswboI03vC6i1Q00FGjIl0Ufb7I616FXF6ePkweTjpB6cnJsdLdt29k+pNh4I8enX750RW8KPKJUBzLynADra641g4a5H33ZYD4FPnuKtuuxlNUZNV92vQp1QNxXaGhAWDvXoBduyLzJlXv1dho/67bGZuI12isqcHN87pSH4qL3eV3LPj9ltGayhlqN5qaUjvzBoAbupWUdM9SARXqr1evtl/PlBn8dCDaTDKfwdfdx/tEr/3H6NF6WeZ0PVp6vZDoQZ+uosur3Fxv8ks18JuaEpeW7no2ncqiOxEDvweTyS76iRKgsSjoToKwK8qG27OpNByizehRunXGfTIM/GS7T6WiA4gWZ6LS1FVlOBkzZhQu/c2kwUGnup+JUNmm0wy+bl+PaGeiR0N3XyIGVhNRB+j9/P7I878TRSLaWFdkQCLfJxntzmv61GUUyRjgy3ZjxG2mnT5eNjT0OivrVG+91OeutJt0LUedHuflfu5l4XRfLPI4k/r8TCWD5x6ErlBejpU+3ZXU7miY6TgCX12duPXc8dDVkWu3c3sT5TKa6XSXgd+dxDLA05V9HOIlE/O0O0gnA98r5eXe1wDn5SXmLPFkYBjoTUDr8IuLvR+n5oVwGOOI5oqbTGJV/rubdKrr6ZSWRONm3ANEuujrjPhMkFXpmp6cHPzf7UhRFXVT6GjxeO3/4yFRz6Vb+SSLNDfvhGQRDgOMHWt9z7QKn8gZ/HScLendGz+pgney9N3re/F65RZ+utS5VKUlWryZ6Pqd7rMd6VTv0gldvqR7PvXr5/3e4cO936vKvmRjGPb+eNCgxIafl+dNJieLWAZ0+Qxud9KVui4z+N6JZuDTwK+bi77T9WTkW7b0F/Qefn/ssmDkSIAPPgDYujUxkxLZkJ+ZQgaqkEK6kiwBm0wo/O5WKNIdEcLJJxtn8AFin6nrzjaYqXmaCjJxgElIT7K13SVjQCJb8wrAm4HPXfS9DjomK8+66smYLqTbZFgqZ/DTqVySjczgCylvtKkKN1nhZ4MAqapCV9FkkU55lK4uoemUR16JJc3pmu89jbo6gM2bJW+I7lTOBw4E+Prr7okrVVRX44aUsRzb2J0MGoSbTHqF14/aWtwA0sv58l7p3Tu1yymSia5si4vxNI+ysugDJjzv011epUv6AgE88ai2Nv4wvHpJeNVrxMDvHsTAFwAgcSN83UU6u+hTuJlMnz7274nMp1jPX+0O0tFFPxPrUCxpTvQmn0J85OUB1NdHXk+3NpqNFBcndyA1HQgGAfr3935/d8uCcDj+49JifTcv1NQkNrx0QzXe/X6AAQOs79xFn9cF7qKf7vv4pGqpiVNaGhoSE05Xfk8E6b4EMN2QLlxI6xn87nDJkhl8byTqvQKB7M2jREFrEbMVdUS9O9fgC97I5vrnhtQRIRpSR+LDS775fPojI30+66OGl47eo9lUR/x+awNEN2JZnhdP/sT7DO0/EG8YmYrM4AsJqfDDhqEbXjJIZoNMhnGRrQIkUe81aBDA7t2JCSsRpGq9nFu8w4fj+eSZRixGYSpcLbO1bSaDwYPT/5SVZCH1RHBC6kb8eMm7AQNwiUJrq/16WRnOiJeUAGza1D2DxFLWSF0dehpF69+T6aIfr41RUIAbspaWWvH3FHpo9y0kGq/r6xJBMtyzZJOc7iMnB9fcqR14T8GLYRsKJXZdZ3eR7i766bb5UTpTVJTqFKQOqSOpIxPyPhPSmI54yTc6IpKOZ+PPVlQkPElJI5vqSCAQ/Wi9WPrWePKmKzZGeXn8z2YyPdQJT+CkuyBS01dQkN4u+tkICe9szKtUvVNPzsu8PPtAh2Ekf2Op3NzsrcNCYpE6IgiJJxl6WzLX44uLvnfy85O/yZ4QGzKDL8DIkalOgTuqMKirw515kxW+0LPo7vqfzTu6jhzp7Z3olIZ338X7hw1LvsdCTQ16jvRUt3PBG+neH2Y76S4TxUBJP9LRwO9p1Nd3baf+7qInlWnGzuBv27YN5s+fD+FwGEpKSuCUU06B3R4X9pqmCYcffjgYhgFPPPFEchOaAeTk4CdT8PlwNi5RyBr8nk2q6n821pOcHG8GtGHYXe5yc5O/qZsapyDoyLT+MNvIRrko9Dx6Wj2ORS9PZd70pHLJWAN//vz5sH79eli6dCk89dRT8OKLL8Jpp53m6dlbb70VjJ5UyoIjtKN7Io8qysaqJbMWiSMVm8sJgiBkAukuF9M9felMspZWpluZJEOvzBayeblnupGRzorvvPMOPPPMM/Daa6/BuHHjAADgjjvugCOOOAJuvvlmqHXxE3njjTfgl7/8Jbz++utQk+0HjmYJyRQEPh/A2LGJDTNbBVe2vleqkE4OkXwQBIGTDueHC4nHMDKrbOPtl5KhVwqJoSfpGhlp4K9YsQJKSko6jXsAgJkzZ4LP54OVK1fC0UcfrX1uz549cPzxx8Ndd90F1R4Xce/btw/27dvX+b2lpQUAAFpbW6E1TbcBp3Sla/pioaMDO4RMeZX2dkxzW1t2nSPd2orl0N6euLLIpnoaCx0d1qe1tWd1OCqmaeVDOtJT66iQOWRTHaX+M5H9TKLJNJ0kHaC6aZqtCSnbtjYsg7Y2rCuJ1k0ArHDb2qSsE4lpJqe8Yo1fjTuT5KjXNBqmmUnjaci1114L999/P7z77ru265WVlXD11VfDmWeeqX3u9NNPh/b2dvj9738PAACGYcDjjz8O3/nOdxzj+tnPfgZXX311xPVHHnkE8pO97bMgCIIgCIIgCILQ46HJ6p07d0I4HHa8L61m8C+77DK44YYbXO9555134gr7ySefhGXLlsGaNWtieu7yyy+HCy64oPN7S0sL9O3bF2bNmuWasamktbUVli5dCoceeijkZPhuQW+8gaNtTU2pTknPZv9+gLfewvNE6+sTE2Y21dNYePVV3Ixm71504+tBrx7BunUA33wDcOCBqU6Jnp5aR4XMIZvq6O7deLJGbS2eepGOiE4SO1RHe/U6FPr3z4Gqqq6Ft3kzwOefA/Tvj/3HZ58BDBkCUFSUmPQCYJhffgkwdKhsztoTyCQ5Sp7k0UgrA//CCy+Ek046yfWe/v37Q3V1NWzZssV2va2tDbZt2+boer9s2TL473//CyUlJbbrxxxzDEyZMgWWL1+ufS4UCkFIc35TTk5O2leCTEhjNMjNPcNfI+MxTSwLvz/xZZEN9TQWfD57XvagV4+A8iLd86Cn1VEh88iGOhoIoFxMRj+TKEQniR+fLwf8/pwu5x3Vk5wcnHzw+fBaIsuE4kh0uEJ6kwly1Gv60srAr6iogIqKiqj3TZw4EXbs2AGrV6+Gsf+3k8WyZcugo6MDJkyYoH3msssugx/96Ee2a42NjfCrX/0KjjzyyK4nXkgKgwfjGigh9fTkteLJQPITkXwQBCFTGDQI1+ELsZMoWV9ZiWu4w2GAr79OTJgqtbU4cCArcYVMJa0MfK8ccMABcNhhh8Gpp54Kv/71r6G1tRXOOeccmDdvXucO+hs3boQZM2bAAw88AOPHj4fq6mrt7H5dXR00NDR09ysIHkmky5UgpBOye7wgCIKddJeJaboyMyNIVNn6fAC9eyc+XI7fb49DEDKNjN3n++GHH4ahQ4fCjBkz4IgjjoDJkyfDb3/7287fW1tb4d1334U9e/akMJWCkD2ku+KVKVA+Sn4KgiBY+P0oFwMZOfUkRCNZfZ70pYIQScaK0dLSUnjkkUccf6+vr4doBwRk4AECgpAypBNNPJKnkgeCICD5+QADBgAUF6c6JUKySIbaLd5wghBJxhr4giAImYjM4NuRfBAEgVD2QRayCJH1gtB9ZKyLviAI3YfPh51zQUGqU5I9iLIjCIIgCPGTl4d9aZpvfC4I3Y7M4AuCEJVAAGDkSOlEhcQjAx2CIAjZTzJkfa9eAI2NAMFg4sMWhExGZvAFQfCEGPeJQVz07Ug+CIIgZD/JkvVi3AtCJGLgC4IgpADDwKUPPRnNyaWCIAhCFmIYsoGiIHQX4qIvCILQjdAsxtixqU1HOlBVhR9BEAQhuxk9WjwBBaG76OHzR4IgCIIgCIIgCIKQHYiBLwiCIAiCIAiCIAhZgBj4giAIgiAIgiAIgpAFiIEvCIIgCIIgCIIgCFmAGPiCIAjdiBwLJwiCIAiCICQLMfAFQRAEQRAEQRAEIQsQA18QBKEbkRl8QRAEQRAEIVmIgS8IgiAIgiAIgiAIWYAY+IIgCN2IzOALgiAIgiAIyUIMfEEQBEEQBEEQBEHIAsTAFwRBEARBEARBEIQsQAx8QRCEbkRc9AVBEARBEIRkEUh1AgRBEHoSBQUAJSWpToUgCIIgCIKQjYiBLwiC0I0MHZrqFAiCIAiCIAjZirjoC4IgCIIgCIIgCEIWIAa+IAiCIAiCIAiCIGQBYuALgiAIgiAIgiAIQhYgBr4gCIIgCIIgCIIgZAFi4AuCIAiCIAiCIAhCFiAGviAIgiAIgiAIgiBkAWLgC4IgCIIgCIIgCEIWIAa+IAiCIAiCIAiCIGQBYuALgiAIgiAIgiAIQhYgBr4gCIIgCIIgCIIgZAFi4AuCIAiCIAiCIAhCFiAGviAIgiAIgiAIgiBkAWLgC4IgCIIgCIIgCEIWIAa+IAiCIAiCIAiCIGQBYuALgiAIgiAIgiAIQhYgBr4gCIIgCIIgCIIgZAFi4AuCIAiCIAiCIAhCFiAGviAIgiAIgiAIgiBkAYFUJyDTME0TAABaWlpSnBJnWltbYc+ePdDS0gI5OTmpTo4gaJF6KqQ7UkeFdEfqqJDuSB0V0p1MqqNkf5I96oQY+DGya9cuAADo27dvilMiCIIgCIIgCIIg9CR27doFxcXFjr8bZrQhAMFGR0cHfP7551BUVASGYaQ6OVpaWlqgb9++8Omnn0I4HE51cgRBi9RTId2ROiqkO1JHhXRH6qiQ7mRSHTVNE3bt2gW1tbXg8zmvtJcZ/Bjx+XzQp0+fVCfDE+FwOO0rqiBIPRXSHamjQrojdVRId6SOCulOptRRt5l7QjbZEwRBEARBEARBEIQsQAx8QRAEQRAEQRAEQcgCxMDPQkKhEFx11VUQCoVSnRRBcETqqZDuSB0V0h2po0K6I3VUSHeysY7KJnuCIAiCIAiCIAiCkAXIDL4gCIIgCIIgCIIgZAFi4AuCIAiCIAiCIAhCFiAGviAIgiAIgiAIgiBkAWLgC4IgCIIgCIIgCEIWIAZ+FnLXXXdBfX095ObmwoQJE2DVqlWpTpKQBVx33XVw4IEHQlFREVRWVsJ3vvMdePfdd2337N27F84++2woKyuDwsJCOOaYY2Dz5s22ezZs2ABz5syB/Px8qKyshIsvvhja2tps9yxfvhzGjBkDoVAIBg4cCPfdd19EeqSeC9G4/vrrwTAMWLhwYec1qaNCqtm4cSMsWLAAysrKIC8vDxobG+H111/v/N00TfjpT38KNTU1kJeXBzNnzoT333/fFsa2bdtg/vz5EA6HoaSkBE455RTYvXu37Z633noLpkyZArm5udC3b1+48cYbI9KyZMkSGDp0KOTm5kJjYyM8/fTTyXlpIWNob2+HK6+8EhoaGiAvLw8GDBgAixcvBr4nt9RRobt58cUX4cgjj4Ta2lowDAOeeOIJ2+/pVCe9pCXpmEJW8eijj5rBYND84x//aK5fv9489dRTzZKSEnPz5s2pTpqQ4cyePdu89957zXXr1plvvPGGecQRR5h1dXXm7t27O+8544wzzL59+5rPPfec+frrr5sHHXSQefDBB3f+3tbWZo4YMcKcOXOmuWbNGvPpp582y8vLzcsvv7zzng8//NDMz883L7jgAvPtt98277jjDtPv95vPPPNM5z1Sz4VorFq1yqyvrzdHjhxpnn/++Z3XpY4KqWTbtm1mv379zJNOOslcuXKl+eGHH5rPPvus+cEHH3Tec/3115vFxcXmE088Yb755pvmt7/9bbOhocH85ptvOu857LDDzFGjRpmvvvqq+dJLL5kDBw40jzvuuM7fd+7caVZVVZnz5883161bZ/7pT38y8/LyzN/85jed97zyyium3+83b7zxRvPtt982f/KTn5g5OTnm2rVruyczhLTkmmuuMcvKysynnnrK/Oijj8wlS5aYhYWF5m233dZ5j9RRobt5+umnzSuuuML8y1/+YgKA+fjjj9t+T6c66SUtyUYM/Cxj/Pjx5tlnn935vb293aytrTWvu+66FKZKyEa2bNliAoD5wgsvmKZpmjt27DBzcnLMJUuWdN7zzjvvmABgrlixwjRNFNA+n8/ctGlT5z333HOPGQ6HzX379pmmaZqXXHKJOXz4cFtc3//+983Zs2d3fpd6Lrixa9cuc9CgQebSpUvN5ubmTgNf6qiQai699FJz8uTJjr93dHSY1dXV5k033dR5bceOHWYoFDL/9Kc/maZpmm+//bYJAOZrr73Wec/f//530zAMc+PGjaZpmubdd99t9urVq7POUtxDhgzp/H7ssceac+bMscU/YcIE8/TTT+/aSwoZzZw5c8wf/vCHtmtz584158+fb5qm1FEh9agGfjrVSS9p6Q7ERT+L2L9/P6xevRpmzpzZec3n88HMmTNhxYoVKUyZkI3s3LkTAABKS0sBAGD16tXQ2tpqq39Dhw6Furq6zvq3YsUKaGxshKqqqs57Zs+eDS0tLbB+/frOe3gYdA+FIfVciMbZZ58Nc+bMiahHUkeFVPPkk0/CuHHj4Hvf+x5UVlZCU1MT/O53v+v8/aOPPoJNmzbZ6k5xcTFMmDDBVkdLSkpg3LhxnffMnDkTfD4frFy5svOeqVOnQjAY7Lxn9uzZ8O6778L27ds773Grx0LP5OCDD4bnnnsO3nvvPQAAePPNN+Hll1+Gww8/HACkjgrpRzrVSS9p6Q7EwM8itm7dCu3t7TbFFACgqqoKNm3alKJUCdlIR0cHLFy4ECZNmgQjRowAAIBNmzZBMBiEkpIS2728/m3atElbP+k3t3taWlrgm2++kXouuPLoo4/Cv//9b7juuusifpM6KqSaDz/8EO655x4YNGgQPPvss3DmmWfCeeedB/fffz8AWHXMre5s2rQJKisrbb8HAgEoLS1NSD2WOtqzueyyy2DevHkwdOhQyMnJgaamJli4cCHMnz8fAKSOCulHOtVJL2npDgLdFpMgCFnD2WefDevWrYOXX3451UkRhE4+/fRTOP/882Hp0qWQm5ub6uQIQgQdHR0wbtw4uPbaawEAoKmpCdatWwe//vWv4cQTT0xx6gQB4LHHHoOHH34YHnnkERg+fDi88cYbsHDhQqitrZU6KggZgszgZxHl5eXg9/sjdoTevHkzVFdXpyhVQrZxzjnnwFNPPQXPP/889OnTp/N6dXU17N+/H3bs2GG7n9e/6upqbf2k39zuCYfDkJeXJ/VccGT16tWwZcsWGDNmDAQCAQgEAvDCCy/A7bffDoFAAKqqqqSOCimlpqYGhg0bZrt2wAEHwIYNGwDAqmNudae6uhq2bNli+72trQ22bduWkHosdbRnc/HFF3fO4jc2NsIJJ5wAixYt6vSKkjoqpBvpVCe9pKU7EAM/iwgGgzB27Fh47rnnOq91dHTAc889BxMnTkxhyoRswDRNOOecc+Dxxx+HZcuWQUNDg+33sWPHQk5Ojq3+vfvuu7Bhw4bO+jdx4kRYu3atTcguXboUwuFwp9I7ceJEWxh0D4Uh9VxwYsaMGbB27Vp44403Oj/jxo2D+fPnd/4vdVRIJZMmTYo4XvS9996Dfv36AQBAQ0MDVFdX2+pOS0sLrFy50lZHd+zYAatXr+68Z9myZdDR0QETJkzovOfFF1+E1tbWznuWLl0KQ4YMgV69enXe41aPhZ7Jnj17wOezmwd+vx86OjoAQOqokH6kU530kpZuodu28xO6hUcffdQMhULmfffdZ7799tvmaaedZpaUlNh2hBaEeDjzzDPN4uJic/ny5eYXX3zR+dmzZ0/nPWeccYZZV1dnLlu2zHz99dfNiRMnmhMnTuz8nY4gmzVrlvnGG2+YzzzzjFlRUaE9guziiy8233nnHfOuu+7SHkEm9VzwAt9F3zSljgqpZdWqVWYgEDCvueYa8/333zcffvhhMz8/33zooYc677n++uvNkpIS869//av51ltvmUcddZT2uKempiZz5cqV5ssvv2wOGjTIdtzTjh07zKqqKvOEE04w161bZz766KNmfn5+xHFPgUDAvPnmm8133nnHvOqqq+QIMsE88cQTzd69e3cek/eXv/zFLC8vNy+55JLOe6SOCt3Nrl27zDVr1phr1qwxAcC85ZZbzDVr1piffPKJaZrpVSe9pCXZiIGfhdxxxx1mXV2dGQwGzfHjx5uvvvpqqpMkZAEAoP3ce++9nfd888035llnnWX26tXLzM/PN48++mjziy++sIXz8ccfm4cffriZl5dnlpeXmxdeeKHZ2tpqu+f55583R48ebQaDQbN///62OAip54IXVANf6qiQav73f//XHDFihBkKhcyhQ4eav/3tb22/d3R0mFdeeaVZVVVlhkIhc8aMGea7775ru+err74yjzvuOLOwsNAMh8PmySefbO7atct2z5tvvmlOnjzZDIVCZu/evc3rr78+Ii2PPfaYOXjwYDMYDJrDhw83//a3vyX+hYWMoqWlxTz//PPNuro6Mzc31+zfv795xRVX2I4OkzoqdDfPP/+8Vgc98cQTTdNMrzrpJS3JxjBN0+w+fwFBEARBEARBEARBEJKBrMEXBEEQBEEQBEEQhCxADHxBEARBEARBEARByALEwBcEQRAEQRAEQRCELEAMfEEQBEEQBEEQBEHIAsTAFwRBEARBEARBEIQsQAx8QRAEQRAEQRAEQcgCxMAXBEEQBEEQBEEQhCxADHxBEARBEARBEARByALEwBcEQRCEHsRJJ50E9fX1qU6GIAiCIAhJQAx8QRAEQchwDMPw9Fm+fHmqkxqVu+++G+67775UJ0MQBEEQMhLDNE0z1YkQBEEQBCF+HnroIdv3Bx54AJYuXQoPPvig7fqhhx4KpaWl0NHRAaFQqDuT6JkRI0ZAeXl5RgxGCIIgCEK6EUh1AgRBEARB6BoLFiywfX/11Vdh6dKlEdcFQRAEQchuxEVfEARBEHoQ6hr8jz/+GAzDgJtvvhnuuusu6N+/P+Tn58OsWbPg008/BdM0YfHixdCnTx/Iy8uDo446CrZt2xYR7t///neYMmUKFBQUQFFREcyZMwfWr19vu2fTpk1w8sknQ58+fSAUCkFNTQ0cddRR8PHHHwMAQH19Paxfvx5eeOGFzmUF06ZN63x+x44dsHDhQujbty+EQiEYOHAg3HDDDdDR0aF9n1/96lfQr18/yMvLg+bmZli3bl1M6REEQRCETENm8AVBEARBgIcffhj2798P5557Lmzbtg1uvPFGOPbYY2H69OmwfPlyuPTSS+GDDz6AO+64Ay666CL44x//2Pnsgw8+CCeeeCLMnj0bbrjhBtizZw/cc889MHnyZFizZk3ngMIxxxwD69evh3PPPRfq6+thy5YtsHTpUtiwYQPU19fDrbfeCueeey4UFhbCFVdcAQAAVVVVAACwZ88eaG5uho0bN8Lpp58OdXV18K9//Qsuv/xy+OKLL+DWW2+1vc8DDzwAu3btgrPPPhv27t0Lt912G0yfPh3Wrl3bGWa09AiCIAhCxmEKgiAIgpBVnH322aZTF3/iiSea/fr16/z+0UcfmQBgVlRUmDt27Oi8fvnll5sAYI4aNcpsbW3tvH7ccceZwWDQ3Lt3r2maprlr1y6zpKTEPPXUU23xbNq0ySwuLu68vn37dhMAzJtuusk17cOHDzebm5sjri9evNgsKCgw33vvPdv1yy67zPT7/eaGDRts75OXl2d+9tlnnfetXLnSBABz0aJFMaVHEARBEDIJcdEXBEEQBAG+973vQXFxcef3CRMmAACu7w8EArbr+/fvh40bNwIAwNKlS2HHjh1w3HHHwdatWzs/fr8fJkyYAM8//zwAAOTl5UEwGITly5fD9u3bY07fkiVLYMqUKdCrVy9bPDNnzoT29nZ48cUXbfd/5zvfgd69e3d+Hz9+PEyYMAGefvrphKRHEARBENIRcdEXBEEQBAHq6ups38nY79u3r/Y6GcXvv/8+AABMnz5dG244HAYAgFAoBDfccANceOGFUFVVBQcddBB861vfgh/84AdQXV0dNX3vv/8+vPXWW1BRUaH9fcuWLbbvgwYNirhn8ODB8NhjjyUkPYIgCIKQjoiBLwiCIAgC+P3+mK6b/3fKLm1w9+CDD2oNYz77v3DhQjjyyCPhiSeegGeffRauvPJKuO6662DZsmXQ1NTkmr6Ojg449NBD4ZJLLtH+PnjwYNfndXQlPYIgCIKQjoiBLwiCIAhC3AwYMAAAACorK2HmzJme7r/wwgvhwgsvhPfffx9Gjx4Nv/zlL+Ghhx4CAADDMByf2717t6c4ACzPAs57770XsXletPQIgiAIQiYha/AFQRAEQYib2bNnQzgchmuvvRZaW1sjfv/yyy8BAHfB37t3r+23AQMGQFFREezbt6/zWkFBAezYsSMinGOPPRZWrFgBzz77bMRvO3bsgLa2Ntu1J554onOfAACAVatWwcqVK+Hwww+PKT2CIAiCkEnIDL4gCIIgCHETDofhnnvugRNOOAHGjBkD8+bNg4qKCtiwYQP87W9/g0mTJsGdd94J7733HsyYMQOOPfZYGDZsGAQCAXj88cdh8+bNMG/evM7wxo4dC/fccw/84he/gIEDB0JlZSVMnz4dLr74YnjyySfhW9/6Fpx00kkwduxY+Prrr2Ht2rXw5z//GT7++GMoLy/vDGfgwIEwefJkOPPMM2Hfvn1w6623QllZWaeLv9f0CIIgCEImIQa+IAiCIAhd4vjjj4fa2lq4/vrr4aabboJ9+/ZB7969YcqUKXDyyScDAG7Wd9xxx8Fzzz0HDz74IAQCARg6dCg89thjcMwxx3SG9dOf/hQ++eQTuPHGG2HXrl3Q3NwM06dPh/z8fHjhhRfg2muvhSVLlsADDzwA4XAYBg8eDFdffbXtBAAAgB/84Afg8/ng1ltvhS1btsD48ePhzjvvhJqampjSIwiCIAiZhGHSLjmCIAiCIAgZzscffwwNDQ1w0003wUUXXZTq5AiCIAhCtyJr8AVBEARBEARBEAQhCxADXxAEQRAEQRAEQRCyADHwBUEQBEEQBEEQBCELkDX4giAIgiAIgiAIgpAFyAy+IAiCIAiCIAiCIGQBYuALgiAIgiAIgiAIQhYgBr4gCIIgCIIgCIIgZAFi4AuCIAiCIAiCIAhCFiAGviAIgiAIgiAIgiBkAWLgC4IgCIIgCIIgCEIWIAa+IAiCIAiCIAiCIGQBYuALgiAIgiAIgiAIQhbw/wEXzwZNgADzPQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 12533 with reward: 1.02\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_alphazero/singleEqn/a/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve a*x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### c*(ax+b)+d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No collector log file found at /Users/Kev/Documents/research/LightZero/data_alphazero/singleEqn/c*(a*x+b)+d/log/collector/collector_logger.txt\n",
+      "Mean Rewards: []\n",
+      "Min Rewards: []\n",
+      "Max Rewards: []\n",
+      "Timesteps: []\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The lists are empty. Please check your data.\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_alphazero/singleEqn/c*(a*x+b)+d\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve c*(a*x+b)+d\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "abel-rl",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zoo/custom_envs/equation_solver/read_learning_curve_muzero copy.ipynb b/zoo/custom_envs/equation_solver/read_learning_curve_muzero copy.ipynb
new file mode 100644
index 000000000..9064174f6
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/read_learning_curve_muzero copy.ipynb	
@@ -0,0 +1,859 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "### Notes\n",
+    "\n",
+    "1. Might want to increase the MAX_STEP and equation length -- muzero might be able to exploit longer things. \n",
+    "2. Check how large the depth search is\n",
+    "3. Action masking, illegal action.\n",
+    "4. Debug the env... there is some shenanigans going on... aren't illegal equations supposedly impossible?\n",
+    "5. Then curriculum learning\n",
+    "\n",
+    "\n",
+    "### x+b = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [-0.026250000000000124, 0.1499999999999999, -0.11000000000000008, 0.4262499999999999, 0.5762499999999999, -0.12625000000000008, 0.02874999999999993, 0.2849999999999999, 0.44375, 0.57375, 0.3999999999999999, 0.4237499999999999, -0.013750000000000082, 0.44875, 0.46624999999999994, 0.45749999999999996, -0.10000000000000009, 0.3112499999999999, 0.012499999999999928, 0.18999999999999995, 0.44499999999999995, 0.19624999999999995, 0.86625, 0.43999999999999995, 0.29999999999999993, 0.59, -0.06625000000000004, 0.46249999999999997, 0.5599999999999999]\n",
+      "Min Rewards: [-0.39000000000000024, -0.19000000000000017, -1.0, -0.2200000000000002, -0.22000000000000008, -0.2200000000000002, -0.2600000000000001, -0.18000000000000016, -0.14000000000000012, -0.22000000000000008, -0.30000000000000016, -0.2200000000000002, -0.2400000000000002, -0.10000000000000009, -0.09000000000000008, -0.09000000000000008, -0.20000000000000018, -0.10000000000000009, -0.20000000000000007, -0.09000000000000008, -0.10000000000000009, -0.07000000000000006, -0.040000000000000036, -0.13000000000000012, -0.17000000000000015, -0.09000000000000008, -0.16000000000000003, -0.09000000000000008, -0.20000000000000007]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, -0.06000000000000005, 0.96, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 0.97, 1.0, 1.0, -0.06000000000000005, 0.97, 0.97, 0.97, 0.97, 0.97, 1.0, 1.0, 0.97, 1.0, -0.050000000000000044, 1.0, 1.0]\n",
+      "Timesteps: [224, 292, 363, 416, 457, 545, 628, 693, 745, 786, 834, 890, 969, 1033, 1092, 1158, 1246, 1311, 1395, 1469, 1529, 1601, 1651, 1704, 1771, 1817, 1905, 1970, 2022]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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97pOdnd3g69rtdtjt9nr3W61WTXf+FgsN0Eym0EImTSYrTyxUJCGBQsJ8PmUnKVr/vWoRt5sm/cXFdAEqLKRjA9BxSk2NzWCnIfhcJWw2+mvSpPb9bjcNupxO4Nix2oNEIQ6kptLzEhK0Iw7E8lx1u2myLwb1p0/ThF/8zm02GkBnZcVG4AoWSaI26qVL4/7XmMTDcfV4qG8oKaFFioIC6jM8Huovk5JosUmN1BzRbwuEMFBRQdfs48fpfpuN2pqSQuHeQrRPTKwvDCh5TL3e2gsHZ85Q/ypJdN3JzKzdhxUUUPsN/pMKC4+HbsP9nQU7XjKbtf/9B/v71NAlO/oMGzYMX3/9da37li5dimHDhgEAbDYbBgwYgGXLlmHq1KkAAK/Xi2XLluGOO+6IdXMZxi92Ow3IKyrUn4zEGz4ffffFxXSxPn2aBjtuNx2XpCQaQGg9D5khLJb6wgBAvg1OJw3Gzp6l4ytJsjiQmEjiQFJSbXFA6wODYKi7yn/2LPU1Lhd9BwkJ9LnT07Xtr+BwkCjHMIyyiLz+4mI6x8SKtdNJY5KkJO2JggJJoj685rpdzYiB3Fzg6FG6v64wIJ7j8UTW1zud8mp/bq4cMSiE6mbNGu5brVb63pn6VFRo+5qkRTR4igZPaWkp9u3bV/3/wYMHsWXLFjRr1gxt27bFrFmzcPz4cbz99tsAgFtuuQUvvfQS7r77bvzpT3/C8uXL8eGHH+Krr76qfo0ZM2bg+uuvx8CBAzF48GA899xzKCsrq64WwDBqY7XSgLy8nC5OTHSpGeKflyeHNooQ/8xMbQ52mPCxWumvrkAgUgpKSsiAyOOhQYfNJotAIry0pjig1d+H1yuv8peW0mcqKZEjjaxW+gzNmulPcHQ4ag+uGYYJn7qpbsXF1E+YzdTf6bGPENQUBsSYqiFhQJKAn36ifr6xiAGBz0f9alERreTn51O/C4T+3TkcJB74fDzhrUtpqTGE+Fii0aFJcGzYsAHnnntu9f8iL//666/HW2+9hZMnT+LIkSPVj3fo0AFfffUV/v73v+P5559H69at8frrr1eXBwSAK6+8EqdPn8bs2bORm5uLvn37YvHixfXMAhlGbUQ4LqMsHo8c4n/6NF1whZNxQgLlEWZmqt1KRg1EWkHTpvJ9YrBYVSULRWKAVjMNISVFjhxwOMJzqxbpCjXzW0OlooIGoIWFtNJfVkZtlyRql0iB0HtVCY6YCkxVlRw+yyiPEueq2lRUyKluRUW1r4NNmlD1DaNORP0JAx4PcPIkbQeKGBCiQFUV9bO5ufQ9VlbK14OWLcOLGLTb6ThUVipXIcoICKHFT2Y2EwBdiwBjx46FL0AtiLfeesvvczZv3hzwde+44w4O/2c0jc1Gk1MmcmqGNp49SwOesjKKthAX7NRUDvFn/FNzsFhXHBBGhCLss6Y4EM7vSVzufvop/MG3MEa0WGigmpKiroFftLBa6bNyxFR9qqqAdevou2GigxLnqtqIvqJmlFM8XwfFcRSGgkDgVAKXSxZNUlIoRSJS7HbZN4BFABkhxKvpwaRHdC0CMEy8YrfTpFW4cTOh4XTKoY2nTsmhjSaTvMLBYWVMJNRcWa+J10uDlXBqZHu99NtNTAx/MJ6aqt30BKUR5oBMbcQKb5MmfP2IFkqcq2oTT31FuARKJTCblf/+zGZ6fe7XauN0kuii4YJsmoRPb4bRIQ6HPHH1Z2zGNExJCbBpE4XpxUtoI6MdTKbwVyu8XooqSEjQ78QillitbA7oDzFg5mtH9OBzNX4RwkA0X7+0NHqvr0cqK6lPY9EqNPjrYhgdYrfLZW54IBcaZWU0OGvZklfBGMbIOBwU7cMRU7VxOtVuAcMw4SLMARkZ0afxQk5osD7JMDpEkmilgXM6Q0d8ZzwpYBhj43DQ4JBDZ2tTWcmDZYbRKw6HbOjKENynhQeLAAyjUywWCm1nQqOoiPP9GSYesNtZBPBHcTH3gQyjVxwOmvTyIpBMaSmnAoQDiwAMo1PsdrleLBMcXi+JAOwgyzDGhyOm6uPz0YCZyyYyjD6xWKhqA4ubMiUl3KeFA4sADKNTHA66CHB+Z/BUVtL3xbVkGSY+4Iip2ohSWhwJwDD6RZIoJYCR+zQWAUKHRQCG0Sl2O4eEhUp5OX1nLAIwTHxgt8uVQBgSQauquA9kGD3DlU9kRJ/GwmbosAjAMDrFYqFQVw4JC56KCpoMcMkmhokPHA4S/zhiihDlATl/lmH0iygT7fGo3RL1EX0aRwKEDg+FGUbHcEhYaJSVsYMsw8QTbA5Ym8pKuuV+kGH0i+jXOBKUvgefj/u0cGARgGF0jDAHZILj7Fk2BWSYeMJiodUyHiwTXEqLYfQPi5syHOUVPiwCMIyOsdvJ6dnlUrsl2qeqiiIBWARgmPiCI6ZkuJQWw+gfSaLVbxY3uU+LBBYBGEbHiHqxrAY3jqikwIZYDBNfcMQU4fNRpQTuAxlG/1it5AsQ73DJ0/BhEYBhdIzNRivcrAY3Tnk5RUywgyzDxBciYsrtVrsl6uJykRDKfSDD6B+Hg8TNeK584nbTAg+LAOHBIgDD6BxJ4kiAYBCVARiGiS9ExFS8i6WVlVxPm2GMgigTLcw+4xEuDxgZLAIwjM6xWoGiIrVboX2KinjwyzDxiM1Gq+DxLpY6nbRyxgNmhtE/QgSIZ3GThc3IYCsFhtE5DgdQWAh4vYCJZT2/eL0kArApIMPEL/E8WAa4lBajPh4PsHkzkJ8PZGQA/foBZrPardInZjONbcrLgfR0tVujDqJP47FveLAIwDA6R+S7VlYCiYlqt0abVFTQ99OkidotYRhGDdhEi8sDMuqyfDkwbx5w6pR8X2Ym8I9/AOPGqdcuPWMyxXflEy4PGBmsnTCMzhH1YuN9lSsQXBmAYeKbmhFT8UpJCZfSYtRh+XLg7rtrCwAA/X/33fQ4EzoOB3DmjNqtUI+yMo4kiQQWARhG55hMNLCN93zXQAhTQA4ZY5j4hE20SATg3Fkm1ng8FAEQiGeeof2Y0HA4aAGoqkrtlqgDlweMDB4SM4wBMJtpgMf4p6SEw2AZJp6JdxOtqio20GLUYfPm+hEAdcnLo/2Y0IjnyiceD31uNjoNHxYBGMYA2O0U6sr4p7CQTQEZJp4xmSgaKF4jpriUFqMW+fnK7sfIWCxU8SMe+zWnk6q+sLAZPiwCMIwBcDgoN4pNUupTVUVqMYsADBPfSFL8RkzxgJlRi4wMZfdjaiNJ8WkOyMJm5LAIwDAGwOGgDjEe1eDGYFNAhmEA2RwwHqms5PKAjDr060dVAAKRlUX7MaFjtcZnv+Z0UkoAm52GD4sADGMAREhYPOaFNYYwzWG1mGHim3iOmIrHz8xoA7OZygAGYuZMdnkPF4eDyp/Gm7Ei92mRwyIAwxgIFgHqU1HBq18Mw8R3xFRpKQuhjHqMGwfMnVv/WpyaSvePG6dKswxBvJoD8tguclgEYBiDYLcDZ8+q3QrtUVTEg1+GYShiyuWKv8EyQF4I3A8yatKvH6WkAMCAAXR77rksAESKzUb9WryJmyUlnOYZKSwCMIxBsNupU4y3kLBAeL0kArApIMMwAFUJiDcRwOWilUIeMDNqsn8/3bZqBVx7LW2vWycLA0x4SBJ9h/HUr3m9HN2kBCwCMIxBiNeQsEBUVNB3wiIAwzBAfJposYs2owUOHqTbjh0pEsBqBU6cAA4fVrddRsBiIV+AeKGqiv642klksAjAMAYhXkPCAiEqA/CFgmEYID5NtCoruTwgoz4HDtBtx45AQoJcDWDtWvXaZBQcDkoHjZeoCu7TlIFFAIYxCJJEIVIcCSBTXk4XRRP3dAzDgELinc746iedTi4PyKiPSAfo0IFuhw6l2zVr1GmPkbDbaWIcL4tATidVxOLygJHBQ2OGMRBWa3yFhDVGaSkPfBmGkREiQLwMlgEupcVoAxEJ0KkT3Q4bRrcbNvBvNFLiUQRgIodFAIYxEKJCQLyEhDVGYSH7ATAMIyNEwXiKBGADLUZtzp6l67EkyZEAnTsDGRk0oduyRc3W6R+zOb4iQSsreYFHCVgEYBgDIcwBKyvVbon6VFXRBZFFAIZhahJvJlrFxSwCMOoiUgFatpSvyZIkRwOwL0DkmExAWZnarYgNXPJUGVgEYBgDIULC4kUNDkR5OZfFYhimPnY7rUrGQ8SUy8XmqIz61DQFrAn7AiiHwwGcOaN2K6KPz0fRTdynRQ6LAAxjIMxm6iDjJS8sEBUVZBzDajHDMDVxOOTyoUaHywMyWqAhEWDIEIoI2LcPOHUq9u0yEg4HLX5UVandkugiygNynxY5LAIwjMGQJFJJ4x0WQhiG8Uc8mQMKEYBXzRg1aUgESE0FevakbU4JiAyRDmr0SFDu05SDRQCGMRgOB4W6xjuFhawUMwxTn3gy0RLlAblMKqMmdSsD1ET4AnBKQGRYLJT+Y/R+TZQH5PFd5PBlgWEMht1OkQBGDwkLhNcLFBWxKSDDMP6RpPgw0YqHlAdG25w5I1cGaN++/uPCF2D9esDjiWXLjIfJFB8igM/H1QGUgEUAhjEYDkf8hLo2REUFfQcsAjAM4w+Hg8qWGZ3SUlohZBi1EFEANSsD1KR3b6BJExLud+2KbduMhtVq/H6NhU3lYBGAYQyG1SqXx4tXhAjAOWMMw/jDbqdIAJdL7ZZEl9JSDptl1EWUB6zrByCwWIDBg2mbUwIiw+Gg8nlGjqgoK+M+TSl0LwIsWLAA7du3h8PhwJAhQ7B+/foG9x07diwkSar3d8EFF1Tvc8MNN9R7fMqUKbH4KAyjGJIU35EA5eWUEsB5sAzD+CMeTLTcbroOcJlURk0OHqRbf34AAvYFUIZ46NdKSniBRyl0PUT+4IMPMGPGDDz44IPYtGkTcnJyMHnyZJxqoM7Ip59+ipMnT1b/bd++HWazGVdccUWt/aZMmVJrv//7v/+LxcdhGMWwWuPbHLC0lMy/GIZh/GG1UhSAkcVSLg/IaIGGKgPURIgA27cDxcXRb5NRsdmM3a+5XCRysAigDLoWAZ599lncdNNNmDZtGnr27ImFCxciMTERb775pt/9mzVrhuzs7Oq/pUuXIjExsZ4IYLfba+2XlpYWi4/DMIrhcFB+nderdkvUobCQV78YhgmMJBl7xayykktpMeri8zWeDgAA2dlAhw40ZgkQ0Ms0giTRd27Ufo2FTWXRrV1MVVUVNm7ciFmzZlXfZzKZMGHCBKwJMp7ojTfewFVXXYWkpKRa969YsQKZmZlIS0vDuHHj8NhjjyE9Pb3B13E6nXA6ndX/F/8mY7pcLrg0nHDodlNn4fUGN1n0el21bhntYrPRanhJCZCYGHhf8RvV8m81FKqqKGfMbo9fEYTPVWPCx1VZzGYy0VKz64tm/1teTtd4IH77QrXgc5UoKACKiqyQJB/atnUH/B0OGWLCwYNmrFnjxbhx2ktq18sxFf1aq1Zqt0R5SkupvxZlXpUg1OPq8WjfSybY64nk84lLhL44ceIEWrVqhdWrV2OYiCMCcPfdd+OHH37AunXrAj5//fr1GDJkCNatW4fBwpEEwPvvv4/ExER06NAB+/fvx3333YcmTZpgzZo1MDcQX/zQQw/h4Ycfrnf/f/7zHyQ2NgNjGIZhGIZhGIPxyy8ZmD17BLKzS7Fw4bKA+27alIlHHhmGjIxyvPbaUi4BxzBhUl5ejquvvhpFRUVITk5ucD/dRgJEyhtvvIFzzjmnlgAAAFdddVX19jnnnIM+ffqgU6dOWLFiBcaPH+/3tWbNmoUZM2ZU/19cXIw2bdpg0qRJAb98tSkpAVavBpo1Cy60xut1ITd3KbKzJ8Jk4lgcrXPsGNC3L9C6deD9XC4Xli5diokTJ8JqgBirvDxgwwZjquDBwueqMeHjqiyVlbSyNHx44xFT0SKa/e+uXcDhwxRqzcQWPleJVaso67hr10S0bHl+wH2bNQOefNKH/PxEOJ3nB0wfUAO9HNOKCooCGj4cSEhQuzXKcugQsGOHsuO7UI7r8eM0rtb6+LI4SGMN3YoAGRkZMJvNyMvLq3V/Xl4eshu54pWVleH999/HI4880uj7dOzYERkZGdi3b1+DIoDdbofdTwKy1WrV9KTKYqH8IZMpNBd1k8mq6Q6QISyW0EqpaP33GiwiM4crA/C5alT4uCpDQgJw5gylEKWkqNuWaPS/paWUGsZ9oXrE+7kqVwYwwdTIDzExEejfH1i7Fli3zorOnWPQwDDQ+jFNSJDTnDS8DhkWFRU0to1GnxbscTWbte9JEOy1RLeXBpvNhgEDBmDZMjm8yOv1YtmyZbXSA/zx0Ucfwel04o9//GOj73Ps2DEUFBSgRYsWEbeZYWKJ3U4GefpM+AmfwkLtd9AMw6iPMNEyopO2KA/IpoCMmojKAB06BLf/0KF0u3ZtdNoTD5hMxjUHLC3l8Z2S6FYEAIAZM2bgtddew6JFi7Br1y7ceuutKCsrw7Rp0wAA1113XS3jQMEbb7yBqVOn1jP7Ky0txV133YW1a9fi0KFDWLZsGS655BJ07twZkydPjslnYhilcDjoIlDDs9LweL1UXsjhULslDMPoAZOJUuOMhtNJK4EsAjBq4fMFVx6wJmINb9MmStdhwkOSKBLUSLCwqTy6TQcAgCuvvBKnT5/G7NmzkZubi759+2Lx4sXIysoCABw5cqRe+NGePXuwatUqfPvtt/Vez2w245dffsGiRYtQWFiIli1bYtKkSXj00Uf9hvszjJax26lMYEVF/EyKKypo8Nu0qdotYRhGDzgcFDrr88FQRmRcSotRmzNnaAxiMgHt2wf3nI4dgcxM4NQpYPNmWRRgQsPhoO/fSAhhk8d3yqFrEQAA7rjjDtxxxx1+H1uxYkW9+7p164aGCiIkJCRgyZIlSjaPYVTDYqFSJuXlQFqa2q2JDSLyIUBFT4ZhmGpqRkwZSSx1OikyqoGiRgwTdUQUQKtWwZ9bkkQT///+F1izhkWAcBH9WlWVcVbOWdhUHl2nAzAMExhJMmZeWENUVNDAl42wGIYJBrudBpdG8wXgUGpGbfbvp9tQXf7FxJ99AcLH4aA+wEjjPxY2lYeHygxjYOx244WEBaK0lC8QDMMEj8VCuaZGGiwDlA/MfSGjJqH6AQgGDyYh/8ABIDdX+XbFAxYLhc4bqV+LJ3+rWMEiAMMYGLudJsZut9otiQ2FhfSZGYZhgsVkMtZgGZDLAzKMWoQrAiQnA7170zZHA4SP0fq18nKO8lQa/joZxsAYMSSsIZxOWv0yUl4vwzDRx2Yjc0CjILxgOHeWUYtwKgPURJQKXLNGuTbFG0br10pKeJFHaVgEYBgDY7NRSJjR8l39ISoDsAjAMEwoOBw0wDRKxJQw0OIBM6MWBQVUrtdkAtq1C/35whdg/XrjnJexxm43Tr/m8dAiDwubysIiAMMYHJ8vPiIBKiroYmfRfc0ThmFiiTAHNEo/WVnJLtqMuoRTGaAmPXtSWkBJCbBzp7JtixdEJKgRFoGqqmhBi1OclIVFAIYxODYbKfJGxygDeIZhYovNRoNMIwyWAXbRZtQnklQAgH67Q4bQNqcEhIeIBDXC2IiFzejAIgDDGBy7nQzzvF61WxJdCgtZJWYYJnQkiW6NMFgG2EWbUZ9IRQCAfQEiRZIoEtQI4qbTSSkBHOmpLCwCMIzBESFhRq4b7fFQtAPnwDIMEw5Wq3EipthFm1EbIQJ06hT+awgRYOdOEvmZ0LFYgKIitVsROSxsRge+TDCMwbHbjV8hgE0BGYaJBBEx5fOp3ZLIYRdtRk0irQwgyMqi53u9ZBDIhI7DYYx+raJCjthilINFAIYxOCaTcULCGkKIADzwZRgmHIxiosUu2ozaRFoZoCbDh9MtpwSEh1H6tZISTveMBiwCMEwcIEnUiRqVigoSOlgpZhgmHETElN4Hy04nu2gz6rJ/P922bh25MC9SAtau1f9qthoYoV/z+UjY5D5NeVgEYJg4QISEGZWSEs6BZRgmfETElN7TppxOdtFm1OXgQbqNJBVA0K8fTWRPn5bFBSZ4jNCvcZ8WPXjYzDBxgMNBSqpRzVXOnuVUAIZhIkOSqJ/UM+yizaiNmKwrIQLY7cCAAbTNKQHhofdIUI5uih4sAjBMHOBwUEeq55CwhhCfi00BGYaJBIcDOHNG7VZEhlGFXkY/KGEKWJNhw+iWRYDw0HskqBABOBJAeVgEYJg4wGKhTlTPIWENwZUBGIZRAoeD+siqKrVbEj5lZZwaxaiHUpUBaiJEgC1bjF3qOFroPRJUr+3WA3ypYJg4wWQypghQXk4CB4e/MgwTCcJJW8/9JLtoM2pSUCB79ERaGUDQrh2QnU3i3MaNyrxmPKH3SNDKSjZ9jhYsAjBMnGC16jskrCHKy/kCwTBM5FgsgNut38Gy10v9IYfNMmqhZGUAgSRxSkAkiH5Nr+JmaSn3adGCRQCGiRMcDqrd6/Go3RJlKSrilS+GYZRBz+aAwkWbTVIZtRCpAJ06Kfu6LAJEhiTpUwTw+WjcymO86MAiAMPECXa7vkPC/OHx0AWCB70MwyiB1UrCoh7hUlqM2ggRoEMHZV938GDAbAYOHwZOnlT2teMBm42qKOkNl4v7tGjCIgDDxAlCBNCjGtwQFRWUL8amgAzDKIHDQSKAHiOmKiu5PCCjLkqWB6xJkyZA7960zdEAoWO3k1eD2612S0JDCJscCRAdWARgmDhBkii0ykiRABUVHP7KMIxy6Dliil20GTXx+YCDB2lb6XQAgFMCIkGYnuqtX6usJOGCIwGiA4sADBNH6DnU1R8VFTTwYGNAhmGUQM8RUxUV3Bcy6pGfT6vNZrNylQFqIkSA9ev1t6IdKR4PsGEDsHgx3YYaqWSz0YKJ3vo1p5PHeNGEg8YYJo6w26lCgFE6VVGKiGEYRglEv6i3FTOA+kOOimLUomZlgGiEb3fvDqSk0ELG9u1A377Kv4cWWb4cmDcPOHVKvi8zE/jHP4Bx44J7Db32axzdFF14+MwwcYTDIefRG4GzZ9kPgGEYZTGbyXBUT3i9XEqLURdhCqi0H4DAbAaGDqXteEkJWL4cuPvu2gIAQP/ffTc9Hix6jATlPi26sAjAMDoh0nAwQN/5rnURn4NXvpRHid8aw+gVh4MERp9P7ZYEDxtoMWoTbREAiC9fAI+HIgAC8cwzwV+fa0aC6oWSEhYBogmnAzCMDlAiHAwgJd3jobywZs2Ub2csKS+ngW9ystotMRZK/dYYRq/Y7RQtVVkJJCSo3ZrgcDqpnBaLAIxaxEIEEJEAu3bRhDY1NXrvpTabN9ePAKhLXh7tN3Bg46/ncABlZbR4kpioTBujictF/Rr3adGDIwEYRuMoGQ4GUA59WZly7VOLigq6SHA5LOVQ+rfGMHpEj07aTieZpXF/yKiBzyeLANGoDCDIyAC6dKH3W7s2eu+jBfLzld1PiJt6MQcU0U0cCRA9WARgGA2jdDgYIIe66p3ycjYFVJJo/NYYRo/UjJjSC2ygxajJ6dOUv202A23bRve94iUlICND2f1MJn2VidZSipNIkVy5kn53RhkH8RCaYTRMKOFgwWK3UySAyxVZ29SmsJAVYiWJxm+NYfSK3iKmysuNUfGF0SciCiBalQFqIkSAtWv1ld8eKv36USpeILKyaL9gkSTKs9cDojyg2os9y5cDF10E3HIL8OyzwJVXAu3bA59+qm67lIBFAIbRMEqHgwFyqKueVrnq4vGQezebAipHNH5rDKNX7HZ9RUyVlGhjxYyJT2LhByDIyaFxTEEBsHdv9N9PLcxm8uIJxMyZtF+wOBy0gKIHtBDd1FCK5PHjwOWX618IYBGAYTSM0uFgAK2eu1z6CQnzR0UFXSC4PKByROO3xjB6RZho6SFiyuejtrIIwKhFLPwABDYbMGgQbRs9JWDcOKBNm/r3N2kCzJ0bulmv6Ne0MMFujLKy0AQOpQmUIikiUKZP13dqAIsADKNhohEOJtBzJEBFBeWKcSSAckTzt8YwekOYaOlBLGUDLUZtYhkJAMhVAowuAhw5Ahw9SiHxc+cCF19M97dvH161HodDP2WiS0vVFTYbS5H0+ejYrFwZuzYpDYsADKNhzGbgxhsD7xNqOBhAg8WiovDbpTbl5YDXyzmwSmI2A6NGBd4nnN8aw+gRm42iAPQglgoRgEVRRg18PmD/ftqOlQggfAG2bNHHORouixfT7ZAhNOm/5Rb6f8cO4MyZ0F/PYqEqIlr/zjwe9aObgk19PHkyuu2IJiwCMIzGEWVw6q7ymEzAU0+FrwYXFtJEWo+UlPBkVGkOHwa++oq2mzSp/7jdDvTuHds2MYzaaH2wDFDEApcHZNTi1Ck5dDvalQEEbdoArVrR737jxti8Z6zx+WQRYMoUus3MBLp3p8dWrQr/tbVueup0kgirpggQbOpjixbRbUc0YRGAYTTMTz+RMYnZDLz1FrBwIfDww9Qxer3kxBsOegoJ80dhIfsBKInbDTzwAE0mBg0Cli6l39pjjwGvvAKccw79Xp55Ru2WMkzs0EvElB7yexnjcvAg3bZpE7tJmyQZv1Tgzp2UDmC3A2PHyveLiL1ww9Dtdu2bA1ZWqp/ilJgYuDKBJNFvvrEISi3DIgDDaJTKSsoBA4CrrgK6dQMGDgQuuCDyfDibjQaOeljlqosQLzj0VTlefZUGHMnJwEMP0YV34EBafRg0CLjvPhKili2LbPWBYfSEw0EigNYjpiorOTWKUY9YpwIIjO4LIKIAxowBkpLk+0ePptu1a8MTAO12qq7kdkfexmjhdFK/q1bE5/LlwE03Ndz3i/72uef0HZXKIgDDaJS33qIyJJmZwM03135s+HC6Xb06vNc2mSicTI+RAOXlNOjlSABl2LKFfmsATfazsurv06ULcPXVtD13Ln3/DGN0RDlVrfeTJSVsCsioR6xNAQUDB9IE7OhR4Nix2L53tHG7gW+/pe3zzqv9WPfuQPPm1C+Fkwqhh0hQtaKbfD7gzTepLKDTSdEmjzxS3zS5dWvg44+BSy9Vp51KwSIAw2iQw4eBRYtoe8aM2iowICvgW7eSg2o4mEykBuuNigoyjeH818gpLQVmzya1+4ILgAkTGt73pptIIDhxAnjjjdi1kWHUQkRMaXmw7PORCMCRUYxaqCUCNGkC5OTQtvBOMgobNgAFBUBKipz2IJAkYORI2g4nJcBmo1B7LUeClpcHDsWPBk4njYdefpn+v+oqYP584PzzgS+/pBTJGTOADz6gFBi9CwAAiwAMozl8PjL8c7loxX/8+Pr7tG5NBjweD10swkGYA+qN8nIOfVWKuXNpUt+qFXDXXYH3TUwkdRwA3n5bDgFlGKMiIqa0PFjm8oCMmvh86okAgHF9AUQqwMSJ/hc8avoCiJr1wSLGT1oWN0tKYmsKWFhox223mfHNNxRdcu+9wD/+IX/3ZjNFnowaRb85PacA1IRFAIbRGN9+C6xfTx3g3Xc3POEVF79wUwLsdhrcVlWF93y1KCzkAa8SfPst8PXXNNF5+GH/FQHqMmYM/Xk8wJw52s+VZphIMZnCj7aKBVpw0Wbil5qVAdq1i/37i3HQzz/TeWAEKiuB77+nbVEVoC6DB9MYLjcX2Lcv9PfQsump10tj01iN8/buBe66azS2bTOhaVPgxReByy+PzXurDYsADKMhSkuBZ5+l7WnTArv/i4vf2rWhK8GAnBem5VWuung8lMLAfgCRkZtLk3gA+NOfgL59g3/uXXcBCQnkJfC//0WjdQyjHRyO8OpxxwohArAwyqiBiAJo21ad32DXrkBaGo1jfvkl9u8fDVauJGGlRQugTx//+zgcJAQAwI8/hv4eokKAFoV8Ed0UC2Hzxx+Bm26y4PTpRLRp48Nbb8nfazygexFgwYIFaN++PRwOB4YMGYL169c3uO9bb70FSZJq/TnqzCZ8Ph9mz56NFi1aICEhARMmTMDevXuj/TEYBgDlHBUU0AX1+usD7ztgAHWSJ06Qh0CoWCxkPqPlkLC6VFTQBYLzX8PH6wUefJDC7Xr1Am68MbTnZ2cDf/kLbT//vD5TShgmWOx2ud/RIlptFxMfiLSwDh3UeX+TSfZIMoovwDff0O2UKYHz4iMpFShMT7Vo8huL6CafD3jnHWDmTKC8XMI555zGm2+6VYlmURNdiwAffPABZsyYgQcffBCbNm1CTk4OJk+ejFOnTjX4nOTkZJw8ebL673Cd2dPcuXPxwgsvYOHChVi3bh2SkpIwefJkVGrxTGEMxe7dwIcf0vbddzfeASYkyCu44ebDmUz6igQQ6QssAoTPu++So3BCAvDoo+EZLF51FVUMKCoiIYBhjIoYLGu1n+TygIyaiEiATp3Ua0OkqZFaoqhI/hwNpQIIhAiwYwctHoWC3a7dfs3ppAWqaJk/u1zk+P/88yQG/O53Hjz44BqkpETn/bSMrkWAZ599FjfddBOmTZuGnj17YuHChUhMTMSbb77Z4HMkSUJ2dnb1X1aNelg+nw/PPfcc7r//flxyySXo06cP3n77bZw4cQKff/55DD4RE694vXKO9cSJsrLdGKJUYLgigM2mr5Xcigr6jnjQGx67d8vOtzNnUsRJOFgsVE5Qksg1d9Mm5drIMFrCYqE0JK1GTBUXcyoAox4HD9KtGqaAAjFe2rMn9Mmw1vjuO5oAd+3auLDSvDnQowdNZFetCu19tFwmOprRTYWFwG230bjFZCLzv3vv9cJiCSOn1gDotshWVVUVNm7ciFmzZlXfZzKZMGHCBKwJMCMqLS1Fu3bt4PV60b9/fzzxxBPo1asXAODgwYPIzc3FhBp1slJSUjBkyBCsWbMGV111ld/XdDqdcNb41Rb/VnfN5XLBpWGnErebOgGvN7i8IK/XVeuWUY5PPzVhxw4zkpJ8mD7dHXSe1pAhAGDFxo0+lJe7Q86Vt9uBkhI6nlr+rQqKisiASIt5bFrC37laWQk88IAFbreEMWO8uOgiT0TfY69ewNSpJnz2mRlz5vjw7rtunoxEGe6D1UGSaLLdvLnyry363XD6X1Ee0GrlPlFrxMO5SpUBLAAktG/vUu03mJoKdOtmwZ49EtaudeO886IzoYvFMf3mGzMAEyZP9sAbxBc6cqQJu3aZ8eOPdE0PBUmiSXF2dnhtjRalpdQ2pX9PBw4AM2ZYcOKEhKQkH554woNhw3whH1ePR/smlMFeT3QrAuTn58Pj8dRayQeArKws7N692+9zunXrhjfffBN9+vRBUVER5s2bh+HDh2PHjh1o3bo1cnNzq1+j7muKx/wxZ84cPPzww/Xu//bbb5GYmBjqR4s5p0+Htn9u7tLoNCROKSy04cUXxwMw4w9/2A6X6wBOnAjuuQ4HkJ4+CQUFCVi27Gf06xfiwazB0qX6Oa7Bfj/xTs1z9dVXz8HBgx2RllaJP//5e5w8GXlZiMsus2L58nE4eNCBV17Zi8svZ/+UWMB9cOzZs4f+okWk/a8Ww3oZY5+rp087UFY2GWazFxbLNzhxQr3V1N69e2DPnq5YvvwkcnKiG5oWrWN66lQCtmyZBEnyISfnO5w40XgacrduKQDGYt06Lw4dWgybLbSZ84EDckqH1lBynLdpUybmzRuI8nIJWVlluP/+dWjTpqTWewRzXCUJ2LqV/rRMeZAXBN2KAOEwbNgwDBPJQwCGDx+OHj164F//+hceffTRsF931qxZmDFjRvX/xcXFaNOmDSZNmoTk5OSI2hxNSkoo96hZs+DCCb1eF3JzlyI7eyJMJl7yU4rXXjOjrMyErl19+NOfusNi6R7S80eONOO//wV+/XUILrggtAuAzwecOOGCJC3FxIkTYdXwUq7TCfz0E0UvJCWp3RptU/dc/eknCV9/Td39Qw9Z0L37hEZeIXhmzpQwezbw0Uc9cNllXdCqlWIvzdSB+2B1KCmhVakRI5TPU3W5XFi6NLz+N9RrOBM74uFcPXyY8vLatpXQrt15qrZl4kQJn3wC/PJLa2RnZwc01AuXaB/TpUup0f37+9Cnz7igntOiBdC8uQ+nT1tw8uR5GDYseCGmrIw8lkaM0E6JUZ+PxnkeDxTJ0ff5gA8/NGH+fBO8Xgl9+3oxd64NqamjqvcJ5bgeP05eXFof54iI9MbQrQiQkZEBs9mMvLy8Wvfn5eUhO8jYFqvVin79+mHfb0U2xfPy8vLQokWLWq/ZN0ANLbvdDrsfpzKr1arpSZXFQqqWyRTYgbQuJpPVsBe1WLNpE/DVV3Qc7rtPgs0W+vc6bBjw3/8Ca9eaYTKZw26L1n+vpaV0wUpNDe33Gs+YTFYUFlohNM6rrgJGjFC22z/vPMqv+/lnCU8/bcXzz7NnQ7TReh/s8QCbNwP5+UBGBtCvH6Xx6BWHg8Jm3W4y1IwG4fS/Hg+1yWbjc06raP1cjQTZD0BS/TPm5ACJicDZsxL27bOie2hrKSERrWO6eDHdnneeCaYQBjkjRwKffQasWmXBiBHBv19CAo2rXC7tLKyIygAOR+TjPLcbmDsX+PRT+v+ii4D77jPBavX/wsEeV7NZ+6JrsNcS3Q6lbTYbBgwYgGXLllXf5/V6sWzZslqr/YHweDzYtm1b9YS/Q4cOyM7OrvWaxcXFWLduXdCvyTDB4nIBTz5J27/7HdC7d3ivM2QIdUoHDwInT4b+/Gg5sCpNRQUNevXSXi3g81EFgDNnyLjpjjuUfw9JAu65hy6Kq1cDNbpPJg5ZvpwGW7fcAtx/P91edBHdr1dsNhIgtRZyL4oWsQDAqIEII1fTFFBgtQIDB9J2uEbJarJ3L5VbtFqB8eNDe+7o0XT74490zQ8WUSZaS/1aZWV45QE9HmDDBhJSNmygMc9f/0oCgCQBd94JzJ6t/cl7rNGtCAAAM2bMwGuvvYZFixZh165duPXWW1FWVoZp06YBAK677rpaxoGPPPIIvv32Wxw4cACbNm3CH//4Rxw+fBg3/lYoW5IkTJ8+HY899hi++OILbNu2Dddddx1atmyJqVOnqvERGQPzn//QRTQtDbj99vBfp2lTWUAI5+InglhCuXioQVmZ2i3QH599ZsLKlXThe/xxhGwcGSzt2wM33EDbzzxDqwtM/LF8OZU3rVul99Qpuj8WQkDdwaAnNK8sv4hJttactLk8IKMmWigPWJNIqyWpiYgCGDmSxnShMGgQjePy8khMCBUtja1EJEAoiz3+hOfzzgN+/pmiQ555Brj2Wu4r/aHrNbUrr7wSp0+fxuzZs5Gbm4u+ffti8eLF1cZ+R44cqRVSc/bsWdx0003Izc1FWloaBgwYgNWrV6Nnz57V+9x9990oKyvDzTffjMLCQowcORKLFy+GI1qjZyYuOXkSeO012r7zzshzn4YNI6OSNWuASy8N7bl2OynBlZXayQvzR2GhttunNY4fb4L586n/u/12oEuX6L7fDTfQQOboUWDhQiq9w2iDWITnezzAvHmB93nmGWDMmOilBixfTm2oKUJkZtJvcVxwKbYNYrFQdRItUVrKkVGMOvh8cjpAhw7qtkUgSgVu3UrnRpMm6rYnWLxeWQSYMiX05zscwODBwMqVFA3QtWvwz7XbtVUmWhRaC3bCLoTnugjx9y9/kSMlmPro/vJxxx134I4GYlxXrFhR6//58+dj/vz5AV9PkiQ88sgjeOSRR5RqIsPUY948mnT36wdccEHkrzd8OE281q+n8K5QBoYiEqCiAtCqj6XHQyZYrMUFh8sFPPvsADidEgYPBq6+OvrvabcD995LgsOHH9LvukeP6L8vE5hoTIy9XhIUjh+X/7ZurR8BUJe8PHrPtDRa7WrSpP5fUlLDjzVpIvdX/j6nv8GgiEKYOzcyIUD4Avh82lhREuUBG/o+GP2hJy+NvDxaQTabgbZt1W4N0bo10KYNCdEbNgBjx6rdouDYsoW+zyZNKBIgHEaPJhFg5Urgt+DmoHA4qPxpqOPGaBFKdFMwwvN//kNeSFo9j9RGA4ecYeKLH38EfviBOqV771VmQNm9OxnmFRYC27bR4CFYRLCM1kJda1JRQQqxVkUKLVBzALlypQn796ciOdmHBx+UYmakOGQIrWQsXgzMmQP8+9988VWTSCbGZWVUoun4ceDYsdoT/pMnKUc+HMrKIgs/tdnqCwOJicDatYGfF2kUgoiYqqig91Mbl4v6RI6OMgbRjGKJBiIVoF07beVZDxtGIsAXX9CEUutiCgB88w3djhsXvqgnxIMdO4CCAiA9PbjniUiAiorQ0xCiQSjRTZs3Byc8b94s+0UwtWERgGFiSGWlrFxec41yuXQmE4XCLV5M5myhiAACLeWF1aW8nAa8vOrln/oDSBrxXHSRF1lZsR39TJ8OrFoF7NwJfPwxcOWVMX175jeCWSWZO5cm0SdP1p7kHz8OnD0b+LlmM5CdTaWSWrWilenPP2+8XQ88QBOH0tL6fyUl/u8vLZX7p6oqMn06cyaor6GaSAeDdju9p1ZEgMpK+i70EvLMNEy0o1iigZZMAWsiXO5//JH+AG2LKS6XbKZ7XgRVFps3p8i7Xbvo+nvJJcE9r6bpqRZEgJKS4IXN/Hxl94tHWARgmBjyxhu0upaVBdx0k7KvPWwYiQBr1oRnNNjYoF9NRJSCFsJwtUZDA0jAh/feMyEnJ7aDn4wMqkLw5JPAyy/TezdvHrv3Z4hgVkny84Hbbmv48ZQUeZLfurW83aoVDaxrrth4PCRABnrPrCzgwgvDW5XzeGig6k8gWLcO+N//Gn+NSAaDZjOlQZSXB7/KFk2cTgrh1dIqLBM6WvDSCIf9++lWK34AAF0L//3v+vdrWUz56ScKx8/IAPr3j+y1Ro0iEWDlyuBFADGm0kKFgKoq+gtWBMjIUHa/eIRFAIaJIjVDtN1u4O236f677lK+3rQwxdm9m1asmjUL7fkVFaF1wLGkpERbAyCtEHgAKQHwqTKAvPRSmpRt3w48+yylBjCxJdgJb3o60K0bTexbtqw90Q9lldlsptU2/4IUMXNm+L9Ds5lWqvytVjVvHpwIEOlg0GTSTsSU06kdfwImfPQa0qy1ygB6FVOEIeDkyZG3a/Ro4NVXKTUqlMhJq5WECLVxOmkMGmykVb9+JEY3JjyHExkbL7AIwDBRwl+OH0AhW2PGKP9+6enkDbB7N10Ezj8/tOdXVpIarDURwOejnDVOBahP4wNISZUBpMkEzJoFXHcdsHQple8R5ZtihZ5MtqJBsBPexx9X7rcxbhytttXt97KySACI1ipcrAaDDod2Iqa4PKAx0GNIc83KAFpJB9CjmFJaSqv2QGSpAIJu3eR+cMMGYMSI4J4nfAG8XsTMP8gfojxgsGPQaAvP8YCKh5thjEtD9bIBCtf6/vvovO+wYXQbTp1cj0eb5oBOJ4kTXBmgPloeQHbrRq68APDUUzRpiRX+6gZfdFFs6tRrBTExDkQ0VknGjQO+/JKqlTz2GN1+8UV0w3DFYDAQSgwGHQ6KBAjXFFFJSkq04ebNRIYeQ5rz8uiabLFopzKAlq+FDfH99zS+ad+erpeRIkmyQaAQF4LB4aDrcyyv0f4IJ7pp3DhgwoT692dlaTP9Q2uwCMAwChNsWJqoY6okNUUArzf052sl1LUmojIARwLUR+sDyJtvpovx8ePAm2/G5j0bEuBEXmi8CAGxmhg39N4DB1KliIEDY7MSI6IQ6gofkkTpKEoMBu126ou0IJaGYqDFaBe1xLpIEH4AbdtqR4jS+rXQH6IqwJQpykX1jB5NtytX0oQ6GOx2ORJUTZzO8J6Xl0e3114bO+HZKLAIwDAKE0pYmtL06UPuuIWFlBYQClYrUFSkfJsipaKCBBOtDDa0RL9+jXlL+FQdQCYlyRPRt9+WQ0ijhZoCnBYZN44GRXUx6ipJzSiERx4hTwOfTzk3f6uVwlXVHiyHaqDFaBezmfLBA6G1kGYtVgYIRkzJzNSOmJKfTyH7AIkASjFwIE3q8/KAvXuDe47JRP2k2uJmaWnoRqfFxVQWEaDIw1gKz0aARQCGURg1w9IsFmDwYNpevTq059rtJAJobYKkxegErfDTT4Eu3LQMoPYAcuxYci12u2lFNtjViVAoLCQfjKeeUk+A0yoijSYjIz5WSUQUwvnny74ownxLKdQeLAsDLa4MoH88HirpBvgXq8aM0d65qkURIJjIp+Tk8FeblWbJEorW7NOHKq8ohcMhjwFFicRgMJkoukhNSkpC79PWr6fvsWNHEreZ0GARgGEURu2wNJESsHZtaM/TUqhrTdgU0D95ecDDD9P2yJH1V0EyMirw1FMe1QeQkkRh+A4HsGkTrdRu2EATsw0bQhOdfD7g2DGqq/zKK8Df/04TvQkTqCzhp58G9zpayguNNitW0O3EifG3SiLMtlasUK5fs9nUNwcM1UCL0S5ff00RUsnJJM4JLw1R5nflSnmlUytoUQQAGk4JSkujc2XfPiqJqoWIxyVL6FbJKABBzZSAYBHmgGrhdlNKQqh9mhjnDhmifJviAQ6wZRiFUbtsiRABtm0jZdVfSS1/2O1AQQGFuoZSGiyaeDz0GVgEqI3HAzzwAA1munengY/ZLLvhp6e7kZm5FG3ahFgiIkq0aAHcdBPw4ovAo4/WjgbIzKQVnLpihctFg81ffwX27JFvG4oMaduWhLVNmxpvj5byQqOJ2y0PBMeOVbUpqtC7N5U6PH6cVsUaC7sOBrudQlA9HvXElMpKLg9oBKqqgH/9i7avvx5ITa3tXL9/P4mljz4KvPOONiI/alYG0Ep5wJqMG0fRE3Urw+zeDfztb1S29qabgAULqLSoGhw+DOzcSf3HxInKv/6oUXS7Y4f8HTSGMD1Vy39JRDclJwf/HJ9PFgHEuJcJDRYBGEZh1C5b0qIFuc0eOkShUuPHB/c8MaDUUiRAeTkNeFNS1G6JtnjjDZrsJiYCTzwhq+diAOn1+nDihHrt80fLlnRbNx1AGPbdeit5COzZQ38HDtAkti5WKw0+u3UDunal2y5d6LkeD1UB4LrBxC+/kFCUkgLk5KjdmtgjSTTxf/NNmkwpIQI4HCQCVFSoJ5ZqJaSZiYxPPwVyc2mSduWV9R+fOZMmOfv2AYsWATfeGPs21iU3V64M0KaN2q3xj0gJqkmvXsBrr1GExYED9F0uWKBsKH6wiPSkoUMpSkFpMjKAnj1JaFi1Cpg6tfHniH6tvFwdEaCyMnSfk8OH6fdotQL9+0evbUaG0wEYJgqIsLS6ZnaxMuQKt1Sg2UwXAq1QUUEXBo4EkNm0CXj9ddq+917tlGgKhMcDzJ8feJ9XXiFTvy+/pFV/t5smWQMGAH/4A/DQQ8D//R+tbL/7LkVCXHkl0LcvCQCAuo74WuSHH+h25Mj4NdYUKQGrVysT7mq3U5+kplgajoEWoy3Ky+WKKTfe6L8Eblqa3J+98YYchq8mWqwMECwdO9K1s3Vrig668UYSWGKJzydXBRB9UzQQ0QDBpgRYLOqWiRblAU0hzEpFFEC/flxCOlxYBGCYKDF0qJzvfPfdsTXkGj6cbtesCc2IzeGgfNdomLeFg7ggcdgrUVhIde+9XuDCC2XjM60TTMUMADjnHArVfPppOle+/57CZWfOpM/bpUvjA8+G8kIB4K67tGeyFS18PtkPIB5TAQQdOlDEiMcDfPdd5K8nSfTdqlkhIBwDLUZb/N//AWfO0IQ00Ert5Mk0oXO5yCtAbeNeLacCBEOrViQEdO5MofI330wpArFixw7ytUlIoLSFaCFEgHXrgo8ckiT1jJjDiW4Si1xDhyrblniCRQCGiRI7d9JgMSsL+P3vY2vI1a+fXCYmlLJsdjtNvLUSblpcHD+rto3h85ER4KlTtAoTKN1EawRrxHfllcBf/gKcey6lD4Qr/tQsFffYY5QbDgAnT4b3enpk/35a7bLbeZAkVtyEGVekmM3qOWm7XBQ6y9FR+qWoiHL8AeCWWwILm5JEEV9JSZTe89FHsWljQ2jVFDAUMjJIXD7nHBpj3HorpU7GAhEFMHZsY+V9I6NbNxp7VlbKpQgbw2ZTzxywrCy0sV5VFbBxI23H+/UtElgEYJgo8csvdNunT+zf2+GgMGogtFKBDgcJAGrXwQZo0suVAWQ++IBC+6xWKrWnVO3zWKBGxQyRFzplCvCnP9F9X36pHYEr2ohUgMGDozvY1AOTJtFkavNmZYQgNSOmRO4sRwLol0WLKKWjSxf6bTZGVhbw17/S9ksvQVW/F5EOoGcRACCflJdfJlf5igrgzjvlyKlo4XYDS5fSdjSqAtREkigNDAg+JUD4Avjz4ok2paWh+QH88gv1henpdB4x4cEiAMNEiW3b6FasQsYaoY6G4gtgNlO4oRZEAFGukHO9yNn4+edp+847SeXXE6JiRiCiadg3YgS9flERsHx5dN5DawgRIJohp3ohK0s2jlIiGkDNiCkuD6hvTp8mQRegcnXB5kBfein9hisrgccfV0eA8nrlyEK9iwAAiaPz51PkmctF0XX/+1/03m/9ekoBSU2NTUm7mr4Awfxe1CoTLcacoQibYlw7ZAini0YCiwAMEwV8PlkEUCMSAJB9ATZtCq1TN5nUywuriRhkx3skQFkZcN99NEgZPdq/i7TWUduwz2yW824//TQ676ElTp2idCRJkgeC8Y5YeRPO3JHgcNBkTA2xVBho8cBXn7z+Oh3DPn3kldpgMJmAf/6Trofr1kV3stoQubl0XdZyZYBQsdkosu6ii0jkeOgh4P33o/NeIhVg4sTYmCoOHCinhf76a+P722wUZRTrfi0cYZNLAyoDiwAMEwWOHaNQdqtVvVXbdu2oXKDLJedOBYPdTqGualNeTmFpenMgVpq5c4EjR2g1c/Zs/Q7+GzLsi1XFjEsuITFg82Y5pNWo/Pgj3Z5zDoVLMlQq1WIhN/BIHcHNZpowqOGkHS/pLEbk2DHg889p+/bbQ+/L27UjIzsAePbZ4L1WlEL4AbRrZ6zrssVC19ZrrqH/580DXn1V2WiLigo53SCaVQFq4nDIEQfBpASI36MaIkAoKU4FBVRGGIhNRIWRYRGAYaKAiALo3l29sE1JklVSoZoGg8NBq88uV3TaFSzl5aGVizEiX31FfyYTGdylpqrdosioa9gXy4oZmZnyqvhnn0X//dREDDY5FUAmOZnSQgBlUgLUctLmygD65V//otDnYcNkz55QueYaGleUlJB4GkuMYArYEJIETJ9ORo0AiQDPPENinxL88AMJAa1akTgbK0aPplshDDeG1Rr7MtFOJ50XwQpLwsSxWzegWbPotSseiPMhNsNEByECxLKz94dICQjFHNBup1BXNetgAxRJEc95r4cPA08+Sds33RS9fPlYU9OwL5YVMwDgssvo9n//o9+4ESktld2g47k0oD/ECtzixZEP7tWKmGIRQJ/s2yenotx+e/ivI1atzWbyN1m2TJn2BYORRQCAhIAbb6RSsgClBTzyiDJGeeLYT5kS22g+kXKyc2dwkSMOB429lBI/giHU6CYuDagcLAIwTBRQszJATcQk68gRCkUMBpuNogDUNAd0u2mwG69+AFVV5ANQUUFmUMLdnomMIUNoJaa0VHZpNhqrV9P50749he0yMiNHUqm1kyflPjpcHA76HcUyYsrlogFzPIujemXBAgovnzCBVvIjoWtX4PrraXvu3Nit3BpdBBBceSVN/s1mEozvvTeyNJzCQnniGqtUAEFGBtCzJ22vWtX4/mIRKJYieUVF8MKIz0eeGAD7ASgBiwAMozAVFXLOqVqVAQRNmgA5ObQdSkoAoG4kQEUFXYTitTLAiy9SzltKCoXNx3K13MiYTMDvfkfbn3yibluiBacCNIzDQU7gQOQGgWpETIWaO8tog61bKSfbbJbDzSPlz38moa+ggBzuo43XK4sAnTpF//3U5vzzSWCx2ahPnT49/PSf776jcPfu3emYxRqREhCML4Do12K5CBTKgs/evfSbdzjUX2QzAiwCMIzC7NxJHX5mJpCdrXZrZLU0lJQAq5XUa7WoqKDBbjyueP34I/B//0fbDz/ceGk9JjQuuogG49u3y+ZCRsHlAn76ibY5FcA/okrA0qWRhfmqETElRIB47Bf1is9HUQAAcOGFyk0C7Xbg/vtpBfXLL+XV0WiRm0uTQ6sVaN06uu+lFcaMAV54AUhMBH7+GbjjDjOKi0NX4ERVANH3xBqRErBuXeMRDSYT/WZj1a95vRRRFaywKRazBg7kflAJWARgGIXRih+AQIgAGzYEH7rqcFBN9VjmhdVErK7p1Qk/XPLyaOIPAFdfHVoJKSY40tNlI0KjlQvcuJFWq9LTgV691G6NNhk4kL6foqLQo6P8EetIAJ+PDVP1xNq1EjZtognLTTcp+9p9+wK//z1tP/54dH+LRq0M0BgDBwKvvEJReTt2mHD//SNx+nTwzz9xgiJBJAmYPDl67QxEt25UhaeyksSMxjCZaGIeC6qqQhM2RZ/NfgDKwJcShlEYrYkAXbvSoLe8nC5GwSDqYKuVElBUFH8h8B4P8MAD9Nm7dwfuuEPtFhmXSy+l22++UcfhPVr88APdjh7NE8WGsFioTjcQeUqA1Urna6wwqpmlUfF6gVdeoQvZ5ZdHJzLw9tvpdU+cAF5+WfnXF4iyqh06RO89tEqvXsBrrwHNm/tw5EgybrrJErTHkuhjBg0CmjePXhsDIUnygkKwKQGxMj2trKTFqWBEgMpKKvELsAigFDxMYBgF8fm0JwKYTHItVWFO0xg2G606qSEC+HyUihBvfgBvvAFs2kShh088waFu0WTgQKBtWxLGlCgXpwV8PrkMFPsBBEaE5a5YEVnYa6ydtEtL42sVVu+sXdsCu3dLSEwEpk2LznskJgL//Cdtv/++PP5QmnjyA/BHx47Aq6+6kZ1dihMnJNx4o+z91BA+X+2qAGoiyuOuWkXtCoTDQf1iJGaIweJ0UlpWMP3axo0kGGRns+mtUrAIwDAKcuIEcOYMdWiROgAriSgVGKwIYDLRwFaNCgFOJym+8VQZYONG4PXXaXvWLJqgMtFDkuRogE8+aXxQpAd276Z0koQEWnViGqZXL8prrqwMvn62P+x2ub+KBaHkzjaEx0OpYYsX063Ho0zbmNq43cB77/UAAFxzDZCWFr33GjYMuOAC6scefZTCq5Xm4EG6NXplgEC0agU88cQqdO7sQ34+cPPN5C3TEL/+SuKJzSanoKnFoEE0uc/La9wLR0SCxmL8F4rQIFIBhg2Lv1TRaMEiAMMoiFDhu3XT1iR2yBDqNH/9NbhasQCF48cqL6wmQoHW0vcXTQoLKQ3A6yXTuliXEIpXLryQBmd79pCZp94RVQGGDYufcydcJEk+z4RpVzjE0knb7abIrEiO7fLl1MfccguZyt1yC/2/fLly7WSIr7+WcPx4U6Sk+HDNNdF/v7//HWjWjCad//63sq9dszJAPIsAANCsmRMLF7rRpw+VZrz1VmD9ev/7iiiAUaOoUpOa2O1yRGhjKQEWC4mDsYgErawMfkIvRADxOZjIYRGAYRRE1J7WWumStDSgBy1KBG2GZbdTVEOsqagIPjxM7/h8ZAR46hSFt911l9otih9SU4Hx42nbCOUChR8AVwUIDmHStXZt+PmvsXTSjrQ84PLlwN13U19Tk1On6H4WApTD6QRee428AG64wRuTCWBqKh1HAHjzTSqlphQnT8ZfZYBAJCdTxYchQ2i8cuedwPff197H45FTzbQi7IuUgGB8ASQpNn45JSXB9Wm5uRSNYjIBgwdHv13xAosADKMgIjRMK34ANREpAcGWCnQ46AIXi7ywmpSXx4+p2Qcf0AXZaiUfgMREtVsUX1x2Gd1++y0NRvTKsWOUn2o2AyNGqN0afdC+PQmjHg/V8Q6XWDlpV1aGXx7Q4wHmzQu8zzPPcGqAUnzyCZCXJyE9vQKXXRa7Ejvjx5MI6PFQWkAkJTBrEq+VAQKRkADMnw+cey7lqd9zD5VqFOk2r75KAluTJvLYS22EOeDOnY1HhNps0TcH9Pmo7wymTxOLV716kQjDKEOcDLUZJvpUVsq5VloUAYSb6rp1wQ32YpkXVpOzZ+PDFG/3buD552l7+nRKIWFiS04OhbdWVgJff612a8JHRAH060elrJjgENEAkVQJsNsppSfaRFIecPPm+hEAdcnLk523mfApK6OVeAC48so9MTW4lSSajDZpQhO9999X5nVFZYB4TwWoi80GzJlDKTVeL0X1jR9PaTZvvEH7uN1kxqcFMjKAnj1pu7E2ORwkjCslJPlDlAcMJhJg3Tq65aoAysIiAMMoxK5dNLnOyIhOKaBI6d2bBgdFRdTWxohlXpjA7aYLj9FzmsvKgPvuoxWEMWPkWs9MbKlpEPjpp/o1CORUgPCYPJl+A1u3kqlrODgcdD5HO2IqktcP1gcm2P2YhvnPf0gUatPGh/Hjj8T8/Zs3J1EZoPr2R49G/ppsCtgwFgswe7Ycal83KqiyUlvpNqNH021jhqix8DsRKU6NLfp4PLLvAosAysIiAMMoRM3SgFp0LrVYZEOVYFMCgNjWURfpB0YrD1jXkfupp4AjR4CsLBpAaPH3Ei9ccAENePbvp8mg3igsBLZsoW0uDRgazZtTuUgg/FKRDkdsyqmWlYUfip2Roex+jH8KC4F336XtW27xwGxWR1W85BJyg3c6gccfj1zcjPfygI3h9TbuuK+VdBshVqxbF7iqid1OixTR7NdEecDGIgF27aLFqyZNKB2AUQ4WARhGIWqKAFpl2DC6DbZUYKxCXQUVFeHnvWoVf47cX39NE//HHuPwbbVp2hSYNIm2P/1U3baEw6pVNAjt2hVo0ULt1ugPkRLwzTfhTZYsFhrIRjttqrg4fFPAfv2AzMzA+2Rl0X5M+CxaRGJN167A+PHqhRVJEvDPf9L1e8MG4L//Df+1vF45EqBDB2XaZzT0lG7TtSud604n/TYaI9qRAD5f44sgwg9g0CD2pFAaFgEYRgF8Pu1WBqiJEAF27CBltTHsdhp8RjMvrCbl5cFdFPRCQ47cAH3OaBvvMMEhDAK/+y62opcSiFQAjgIIj/HjaXJ94ACZK4aDJEV3sCzKA4YrjprNwD/+EXifmTNpPyY8Tp0CPvyQtm+7TX1z29atqR0AGdg1NkltiBMnuDJAY+gp3UaS5GiAxlICrFYa/0WLQJEINREiAKcCKA+LAAyjALm5QEEBDaK6d1e7NQ2TlUV5fV5vw7VtaxKrUFdBJKtdWoMdufVDr160QlJVBXz1ldqtCZ7KSjmqh0WA8GjaVHbN/uab8F4j2k7aTieF5kYSITVuHDmZ+yM9XTsO5nrltdfoOPXtq50KHVddRX1bWRnw5JPhRbqIKID27XkVtiH0lm4jRIBVqwL/JhwOEsW9USpwUVbW+HivtFSOsmURQHlYBGAYBRBRAN26aT+fPZRSgTYbTYxiUSHA56MLjlFMAfUUIhjv6NUgcP16EgKys7m6RCRMmUK3S5aEN+C128nQNFqCnjDQikQg9fnkvOUbb6RUpPnzyRehoEB2M2dC58gR4IsvaPuOO7QTyWY2Aw88QJP3H38MrxQmVwZoHL2l2wwcSOPUU6cCexkIc8BgV+xDpbi4cWFzwwbqV9u2BVq1ik474hkWARhGAfTgByCo6QvQ2GRHDGZiEQngdNLFxigigJ5CBBngvPOAxETg8GFg40a1WxMcIhVg9GjtTDz0yMiRQFISiXLCZDEUol1O1ekkcSKScH1RASExEbjhBhI+Ro2iknIA8Pbb4adDxDsLF9JEZcQIigTQEp07A9Om0fbcuaGnOwlTQPYDaBi9pdvY7bJJ9MqVgfeLVr/mclG/1pgIwKkA0YVFAIZRAD2JAH370qA1Pz+4QZ/FEpx/QKSUlxtLBNBbiGC8k5Qkm8R98om6bQkGj0cewHFpwMiw2ylcHqAKHqFis0XXSVuJlThR/WDs2NrRamPH0p/HAzzxRPRCf43Knj3At9/StsjB1xrTptFK/tmzwLPPhvZcrgwQHOPGkchSNyIgK4vuF/2LVhApAYFEAJOJ+oNoiADBRjeJdDcWAaIDiwAMEyFOpxxSpQcRwG6Xy2IFUyVA5IVFO0S6ooIuOEbJO9RbiCAjGwR+/z1w5oy6bWmMbduojU2bAv37q90a/SNSApYtowl9KEgS9Y/RigQoK4tsFdHtBpYupe3zzqv/+F13kQj2yy/6rJChJi+/TLeTJmk3Jcdmk0vRfv018NNPwT2vZmUATgdonHHjgC+/pMiQxx6j2y++0J4AAMg+KDt3AqdPN7yf2Ux5+UpTWUn9bCAR4Ngx4PhxGhOKMSujLLoXARYsWID27dvD4XBgyJAhWB/A7ey1117DqFGjkJaWhrS0NEyYMKHe/jfccAMkSar1N0WMDhjGD7t20SArPR1o2VLt1gSHUFWDEQGinRcmKC83Vkiz2UwhgIHQUoggQ6aePXvS+SxyfLWKSAUYMcI4wpmaDBxIfXhRUfAlVGtisUTPSbu0NDJTwLVrScht1ozKbNUlKwu49VbafvHFwJMCRmbLFppQm81U+lXL9O4N/OEPtP3EEyQsNcaJE3LINlcGCA6zmfqSKVPoVqvX94wMMo0EyCCwIRyO6AjiojxgoCoaoh/u04fSmBjl0bUI8MEHH2DGjBl48MEHsWnTJuTk5GDy5Mk41YAb14oVK/CHP/wB33//PdasWYM2bdpg0qRJOH78eK39pkyZgpMnT1b//d///V8sPg6jU7Zvp9vevfUziRXmgJs3N756Fc28sJqcPRvZQFeLiPDgur8LrYYIMnI0wGefaTc02ueTRQBOBVAGs5lWc4HwqgQ4HNSHKR0x5fFQ3xuJKaBIcZg4sWHB6IorZCf5xqqaMHScFyyg7YsvJuMyrXPrrWSulpcHvPRS4/uLVID27bU7mWXCJ5hSgXY7jWOcTmXfO5jXE34AwseKUR5diwDPPvssbrrpJkybNg09e/bEwoULkZiYiDfffNPv/u+99x5uu+029O3bF927d8frr78Or9eLZcuW1drPbrcjOzu7+i8tLS0WH4fRKaIygB5SAQRt2tBgwO0GNm4MrFyYzTQZiqY5oNtN7tpar6wQCuXl8kDrttv0ESLI0EQwKYnCEIMpo6kGhw6RI7nVygMkJRGh8j/+GNxKaU2iFTElcmfD9UopLwdWrKDtQEGNZjNw//10u2xZ4FxhhqrrbN5MwvVNN6ndmuBISAD++U/a/uijxivTsCmgsRk9mm5FlRl/RMv0tLQ0cASb202VAQD2A4gmug0irKqqwsaNGzFr1qzq+0wmEyZMmIA1QcbylZeXw+VyoVmzZrXuX7FiBTIzM5GWloZx48bhscceQ3p6eoOv43Q64awhaxX/FhPocrngCjW5MIa43aRme73BrXh5va5atwx9f9u2WQBI6N3bDa9XJ7XFAAwbZsLHH5uxerUPnToFPq4mE4XJZmVFpy2lpXShSUnR7uprqCxaZEJ+vhmtWvnwhz+460U5RPNz8rkaPnY7cP75Jnz0kRmffOLF4MFRqvsWBuJ4/vAD9TMDB3qRkOAxzDmjNt26AW3aWHD0qITvv3fj/POD78+tVurDiotDS88QY4SGxgqlpSQEpKaG12esWCGhstKC1q196NnTHfA1OnUCrr7ahHfeMeOpp3zo18/NYbh+8HqBBQvoun/FFR5kZHjrfa9a7YMHDgQuvtiML74w4dFHfXjvPXeDAtP+/WYAJnTo4IGXOxnNHtNw6dQJyMqyIC9Pwvr1bowcWb+/M5koGqmkBGjSRLn3Fv1kQz+rrVsllJVZkJrqQ5cugfutSAn1uHo8ofvGxJpg5566FQHy8/Ph8XiQVWdWkpWVhd27dwf1Gvfccw9atmyJCRMmVN83ZcoUXHrppejQoQP279+P++67D+eddx7WrFkDcwPxUHPmzMHDDz9c7/5vv/0WiTq4goaa/5ebuzQ6DdEhp087cPr0ZJhMXqSmLsaJE9qZMDRG165ZAIbip5+cuPbaxo/rvn3RLyFVUBDd148Vp08n4J13xgMArrnmZ+Tnn1SlHXyuhsfIkU3x0Ufj8MMPwPbty9GsWZQNMUJk2bJSAM3Qt+8vOHHisNrNMRQjRnTD++93xxdfFKBv37UhP39t6E8BACxdGvhczcsL73X/+98hALIxfPivOHmy8bHRhRea8e235yI3NwnPPnsYf/rTjvDe2MCsWtUSv/46CAkJLkye/B1OnKhqcF8t9sG//70FK1eOx5EjDjz//EH88Y+7/O63Z88YAKlITd2AEydyY9tIDaPFYxou/fv3wTffdMCSJUfRseMvfveRJIoaaSxyJBxOnPB//3ffdQfQDeeccxy5ubGp2RvMcZUkKre6dWsMGhQB5UGGbuhWBIiUJ598Eu+//z5WrFgBR40Y5Kuuuqp6+5xzzkGfPn3QqVMnrFixAuPHj/f7WrNmzcKMGTOq/y8uLq72G0hOTo7eh4iQkhIKaWvWLLh8Q6/XhdzcpcjOngiTKYIERQOxYweF0nftKqFjx8kqtyY0Jk0C5s71ITe3CU6eTEK/fsMbPK5lZaR8Dh8enbz9I0corcIo5kOvvGJGVZUJ/fp5cdll/SBJsS0BwOdqZLRsCeTkeLF1qwnr1k3An/+sjVUwr9eFnTt/xJ49FL124YW90Lx5L5VbZSwuvxx4/31g69ZMOBzno06gYEBOnKC67F27Bv8cl8uFpUuXYuLEibD6uRAfOUKVIFq1Cv41BWfPAlu20DDv8ss7omXL4Cze77tPwp13Av/7Xydcdlk79OgR+nsbFbcb+OAD+k6vvdaE7t0n+N1P633wvfdKuOce4LPPumDq1A71frNeL3D8OH3OgQP768b0OJpo/ZiGw+TJEr75Bti8uT1atGjt19fq9GmaJwwYoMx7lpfT3KNJk4ZTQHfsoEXXc8/NRsuW5yvzxg0QynE9fpzKbIfTH8eS4iBdanUrAmRkZMBsNiOvjjyel5eH7OzsgM+dN28ennzySXz33Xfo06dPwH07duyIjIwM7Nu3r0ERwG63w+4nnspqtfq9qGsFi4VULZMpsENnXUwmq2E6wEgRpoDnnCPp7jtp0oQ6sw0bgE2bMjFgQMPH1eEgIcDtpnxppSkrIyEqlN+hVtm2jWpySxIwY4YJZrN6H4rP1fC59FJS+z//3Iw//cmsGWOsn3+m61uvXkBWFh9bpWnfnipE7NwpYdkyK668MvjnOhwkrodz2W9ovOB0hn6NFixfTqGr3bsDHTsG36gRI4DJk4ElSyQ88YQVixZxBQrBV18BR48CaWnANdeYYTIF7hi02gePH09/y5ZJePTR+se4ZmWANm2shrg2K4VWj2k4DBpEXhGnTknYu9eK7t3r75OQQGM0SVKmHxDh9Ha7/36tsJCqbgHAsGGWmP32gj2uZnNkRq2xINi5p25Pa5vNhgEDBtQy9RMmf8MCOCXNnTsXjz76KBYvXoyBQRSePHbsGAoKCtCiRQtF2s0YC1kEULcd4SJOlc2bAxe0t1rJnCoaFQJ8Pur0wzW+0hI+H/Dss7R94YXgFTQdM348eVTk5YVXMi5arF9PIgBXBYgewkBPuOoHi91OOfxVDUeHh0xxcfh9o2h/OFWOZ8wAkpOBPXsoMoIhz4fXXqPtadOiI4jHkrvuko/xu+/Wfmz/frrlygDGxm4Hhgyh7YaqBChtDuh0UqRJQ7+r9etpLNW5M9C8uTLvyfhHtyIAAMyYMQOvvfYaFi1ahF27duHWW29FWVkZpk2bBgC47rrrahkHPvXUU3jggQfw5ptvon379sjNzUVubi5KS0sBAKWlpbjrrruwdu1aHDp0CMuWLcMll1yCzp07Y/JkfYV6M9GnqgoQ9hONBJRoFlEqcNu2jEYHrpIUnQoBlZX0ukYQAZYsoUiAhASqCMDoF4cDuOAC2v7kE3XbIigrA7ZupVHRmDEqN8bATJpEK1TbtgHHjgX/PIeDBrhK9ZORlAc8cYIiWSRJLn0YCunpwN/+RtsLFwIn1bE10RQffwycOkUGuaKUqJ7JyCCxBwBefRU4XMNeRFQG6BhcBgmjY0SpwIYqgthstHKvVL/mdAYupSpEd64KEH10LQJceeWVmDdvHmbPno2+fftiy5YtWLx4cbVZ4JEjR3CyxpXrlVdeQVVVFS6//HK0aNGi+m/eb0VxzWYzfvnlF1x88cXo2rUr/vznP2PAgAFYuXKl33B/Jr7Zs4c6xrQ07ecHNUTnzkBGhg9OpwVbtgQuFWizUY6p0ogatHovD1hZCbz4Im1ffz0r2Ebg0kvp9qefgFwN+GKtXSvB7TajTRsfl+2KIhkZ5KIOkLAXLEpHTDmddI0Jx4dFRAEMHAhkBg70apBLLgH696e+7amnAg/cjU5pKfDvf9P2zTcbQ7QGSOgcOpR+t489Jru1swgQP4wcSWLhrl2BjcKV6tfKyxtOK/D5gHXraJtFgOijaxEAAO644w4cPnwYTqcT69atwxAR1wIq9ffWW29V/3/o0CH4fL56fw899BAAICEhAUuWLMGpU6dQVVWFQ4cO4dVXX61XgYBhADKyA4DeveHXTEUPSBIwbBiN7NauDfwh7HYKTfUoXAChoiJwaJheePddCh3PygL++Ee1W8MoQfv2ZIbk9QKff652a4Aff6RL9pgxXt32OXrhvPPodvHi0Ca/SkZMOZ00OQs1EsDnk0WASIIYJQm47z56/1WrgO++C/+19M5771GZ3Pbt5QghIyCOcUICub9/+indzyJA/JCeTh4zAJ3n/rBa6fevBCUlDQubBw9StI3dTp5VTHTRvQjAMGqxbRvd6jUVQDB0KEn/a9YE7g5EXpjSKQHCcEbPnD4NLFpE23/9q/6jGhgZEfb7+edkjKkWbjewahWdKKNHx/GSbIw491waqB48CPz6a/DPs1rJ40QJnE467qGace3dS5M4q5W8LSKhfXvghhtoe948GsDHG2fPkggAALfcYjyTxJYtgdtvp+3nn6foF+EJ0K6deu1iYodICQjkC1BYKEeKhIvHIxtB+0OkAvTrx+OoWMAiAMOEiRAB9GoKKBg0yAeTyYf9+6WAtajtdlqVUloEOHtW/6GVL79M38s550S28sZoj3PPpZSf/PyGcyZjwebNQEmJhJQUJ845h0WAaNOkiTwwDsUg0OGgFbNIB8sAiQDhCKSivSNHAk2bRt6OadNoMlhQIKc8GR2PhyrnLF4MPPkkhTB37w6MG6d2y6LDFVfQMa6oAP75T1nwvP12qjLBGBvR161fT4s9dbHblVkEqqoKnOLEqQCxhUUAhgmDU6co9NtkonJSeiY1FejShZL9A7mgSxINbJWsEOB2U66lnkWA3buB//2PtmfM0H9UA1MbqxW4+GLaFqGyavDDD3Q7cGCu7lNn9IJw1V+yJPhJvZLmgGVloZcG9HplH4NwqgL4w2ajkHGAzoEtW5R5Xa2yfDlw0UW06n///YAoQjVihDHK2Prjhx9qGwMKTp0C7r6bhQCj06ULpTI6ncDPP9d/3G5Xpl+rrGw4xcnpBDZupG0WAWKDQbszhokuwg+gc2cgMVHdtihBv36nAABr1wbez2JRNhxUmALqVQQQJQF9Phpw6z0qhPHP735Ht2vWhOYWrxQ+H7BiBW0PGaIBh8I4YcQIigg4dQrYtCm454jBshJiaaDc2YbYvJkE6qQkigRQigEDyCgQAJ54glbzjMjy5TTpPXWq/mNvvGHMybDHQ6kegXjmGeX9gBjtIEnA6NG07S8lwGRSZhHI6aTfkb+Umi1b6PHmzYFOnSJ7HyY4WARgmDDYvp1ujTLp69+fRjzr1gXOe7bbKXxfKZfo8nJShcNxv9YCy5fT5MBuB+64Q+3WMNGidWt5ZUINg8Bff6XqBHa7Dzk5AeybGUWx2eSc+mCrBEgS9Y+RrpiJAXeopoAiFWDcOOXF1b/9DWjWjPwG3n5b2dfWAvE6Gd682b/oUZO8PNqPMS4iJWDVKv9jPLOZIjcjwels+DGxCDV0KEdUxgoWARgmDEQkgFFEgE6dziI52YeSEmDHjob3czjk1XslEANlPXb4VVXACy/Q9rXXAtnZ6raHiS7CIPCLL2K/CipSAYYM8cFuN9gMROOIkPrvvqNzPhjMZqqkEgmiMkAoE3mXSw5dF9UNlCQlRa4r/8Yb/sPH9Uy8Tobz85Xdj9EnAwZQlYjTpynNsS4OB3DmTGTvUVHR8HivpgjAxAYWARgmRFwuuYPUe2UAgdlMEwwgsC+AMIdRyheguFi/Tsvvvw8cP041xa+7Tu3WMNFm1Cg61mfOyKH5sUKIAGPHKuA2x4RE//4UnlpSAqxeHdxz7HZy0o4kYiqc8oCrV1OfmpFBA/poMHmyXFd+zhzlosK0QLxOhjMylN2P0Sd2uzwB92eCa7dHvgjUUIpTfj5VNZEkoEaldybKsAjAMCGyZw8NgFJSgDZt1G6NcgwbRhOMQANdi4XCVJUwvfL5aKCsRz+AM2doJQygNAAj+EIwgbFYgKlTafuTT2L3vidPUp9jMgEjRxpoxqUTzGZg0iTaDrZKgMNBQmkkg+XKyoZzZxtCtG/SJETNPFKSgHvvpX57wwbgq6+i8z5qEK+T4X79gMzMwPtkZdF+jLERKQH+RABRJjrcRSCfj8xO/YkAIgqgRw8yq2ZiA4sADBMiNUsD6jGMvSGGDqUJxq5dgetcSxJ15JEiys3osRbswoX0HfToAZx/vtqtYWLF1Kk0Gd+wATh0KDbvKaIAcnJ4cKQWIrR+5crgcmKVcNIOVUAoLZUNvaKRClCT1q2Bm2+m7fnzA18v9ETfvhQOHQgjTobNZuAf/wi8z8yZ0ROWGO0wYgSN8XbtorSAmlgs5BkVrggQKLpJiAAcBRBbWARgVKFmDd4NG/RltGM0PwBBRgbQtSuptYGqBAhzwEjRa2WAfftkc7gZM4xbMoqpT3Y2DZIA4LPPYvOeYmI3Zkxs3o+pT7duVEPd6QwuFcRioWtaJGlTgXJn/bFiBbWvbVuqZx9trrmGyooVFZEQYARee61x4caok+Fx44C5c+tHBGRl0f3jxqnTLia2pKcDvXrRtr9oAJMpMhHA5aofCeD1kik1AAwbFt5rM+HBw1cm5tStwXvLLfS/XkrvGK0yQE1EPlhjvgClpZGbo5WXU+evpwGVKAno9ZJruNFWhJjGufRSuv3f/5QzyGyI4mK5bvLYsdF9L6ZhJEleXQ82JUCSIhMBSkpCE0hFu847LzYRahYL8M9/0nt99ZU8iNcr774rp3j97nfxORkeNw748kuKdHvsMbr94gtjf2amPoFSAuz28M0BnU6KJKgbCfDrr7SwlJhozHG1lmERgIkpDdXgPXWK7te6EJCfTzm6JpOslhqJ4cPpdu1amuj6Q+SFReoLUF6uv3SKVauA9evpIvbXv6rdGkYNhg+nCUFRkezEHi1++olWlDt2pBBsRj0mT6bb9euDM4aLZLDs9ZLQGqwpYEEBtQuQqxnEgt69gSuuoO05c+i6oEf++1/guedo+/bbSdyI18mw2QwMHEi/o4ED9SXSM8owejTdrl9f/5wWi0CBSkk3hNPp30hURJ4OHBh6SVQmMlgEYGKGEWrwilSATp2ApCR12xINcnIoJ7KggNRZf9hslNcVaYWAs2f1lQrgcslhr1dfzZOyeMVsppVCAPj00+i+l1wVILrvwzROmzY06fV6qVxgY0Q6WK6q8m+g5Y+lS6ldvXrF3qz2ttto1fzYMXklXU989x3w+OO0fe21wA030DZPhpl4pXNnSn1zOmVxURCJOWBlpf+FHxF5yqUBYw+LAEzMMEINXmEK2Lu3uu2IFlYrMGgQbQdKCZCkyCIB3G4aIOtJBPj4Y+DIEaBZM2DaNLVbw6jJJZfQpGDLFmD//ui8R1WVXKmD/QC0gVhl/+abxveNZLDcUO5sQ9RMBYg1TZpQFB8AvP02eabohbVrKSXR6yVh729/0190GsMojSQ1nBJgs1HfFM74z190U3k5sHUrbbMfQOxhEYCJGUaowStEgD591G1HNBEpAYFEAKuVwqHDpbycBsh6EQEKC4FXX6XtW2+lgS8TvzRvLodMRisa4Oef6Txp3pyqUDDqM3EipYLt2AEcPRp430gGyyJ3NpjygEePkk+NyQRMmBD6eynB2LH05/EATzzRcCqZlti6lRzx3W46rvfeywIAwwjE9W3VKv8h/KGKmz4fedzUFTY3bqRzsFUrjq5UAxYBmJih9xq8LheVTQGMbV4iQrK2bm24HJbDQRPjcAd7FRWhrXSpzWuvkVFXly7AxRer3RpGC1x2Gd1+9VV0cqFFKsCYMVyBQiukpwODB9N2sAaB4UYCBItox+DB6l4777qLUuR++SX6aTKRsncvMH06nbfDhwOPPMLh/gxTkwEDKDX09Glg9+7aj4WzCORy+S8PKPwAhg5lEU4NeGjBxIx+/RpfQdVyDd69e2lwlpxMZZiMSuvW9PlEGUd/iDrY4U5+xOqYHjr9gwcpFQAA/v53HiwyxODBtHpRWgp8+62yr+311hYBGO0gUgIWL/a/QlYTq5VWv0IlWNNUn08WAWJpCOiPrCyKkgKAF1+sX2NcKxw5AtxxB4m6OTnk+M9mZAxTG5tNXhASZWoF4SwCVVb69zmpKQIwsYdFACZm/PhjwyvLAi3X4BWmgL17G39lTuRmiZzkutjt4ee7AqQiBxPqqgWee44EkdGj5VVAhjGZZIPATz5R9rV37iRzzqQkWpFhtMPYsdT/HT5cf4WsLuFGTJWUBBcltXs3tcNu14Z55BVXkDlhWVnjJsBqkJdH7v8FBUDXrtS3Oxxqt4phtElDvgDhVIjyVx7w5Enqv8xm2YuKiS0Gn8owWmH7djLgAWiCWbcGL0COpOeeG9t2hUI8+AEIhAiwZo3/1S6TiQa24eS7+nw0MNbD4GvNGirTZjYDd96pdmsYrXHxxSRm7djR+IQwFEQUwPDh+kmZiReaNJHzZRtLCRBiaSgRUz4fTaKDOe7i/UeP1oZPidlM5fXMZiqf6a/OuFoUFlIEwMmTFOn24otA06Zqt4phtMvIkRSRtHt3bVNvm40m9aGKAD5f7QgnEQXQu7c2+q94JKS1uEceeSTkN5AkCQ888EDIz2OMw/HjwIwZ1AmMGEFlACWJqgDk51PH8Oij5Cq8ZIn6YY0NsX073Rq1MkBNBgygjl4ote3b19/HbKYVq1ARg+LExIibGVXcbrkk4JVXAu3aqdseRns0a0bC5dKllAd9333KvO6KFXTLqQDaZPJkOuZLlpCjfEPRa3Y7rTqXlwff34nygI2JpB4Pvb9oj1bo2hW45hqqFPDUU3QtUbuvLy0F/vpXSu3KygJefpn8HRiGaZhmzWi8u20bCXrCB8dkonF7KJGg/nxOuDSg+oQkAjz00EP17pN+k3V8dZYLJUmCz+djESDOKS6mFdQzZ2hwMGeOHAY+cKC83/HjwMKFNOkaOVJ7qmBBAbVRkuJDBEhIAPr2pRqxa9b4FwHsdlpdCZWKCrogpKVF2Mgo8/nnwIEDQEoKcOONareG0SqXXUYTwsWLqa9LSors9Y4cocmK2UyiKaM9Rowgb5j8fGDTpoZDWcVgOdQVs6oq6ncCsXEjvX9ysvZ+JzffTJEA4ro+Y4Z6bamspPfftQtITQUWLKAa6AzDNM6oUfVFAID6tlAWgeqWB3S7qQIOwKUB1SSkdACv11vr7+jRozjnnHPwhz/8AevXr0dRURGKioqwbt06XHXVVcjJycHRxuroMIbF5aL6wYcOUfj/c881vCJw3XUUoldQALzySixbGRwiFaBjR+0JFNGisVKBDgeFrYbiZA2QeuzxaNf7AaCL28KFtH3zzTTQZhh/DBhAfVd5efCO8YEQqQADBnC4slaxWoHx42n7m28C7ytJoQ2WKyuDKw8ofmvjx2vP2M7hoJJ7APD++3JVnVjjdgOzZpFQk5QEvPSSf0GbYRj/iNSnn3+undbkcABnzzZujiooKandT+3cSfclJ3MJXDWJyBPg9ttvR5cuXfDuu+9i4MCBaNq0KZo2bYpBgwbhvffeQ6dOnXD77bcr1VZGR/h8wGOPkbt8YiIJAP58AAQ2mzxo+OgjZfNrlUCIAEYuDVgXoc5u3Og/p9XhCD0vDCDhQOvGim+8QVEOHTrUVr8Zpi6SBFx6KW1/+mnwg6KGEKkAWjB6YxpGpK0tXx5YCBXmgMESjKjqdNJKe812aI1hwyhNweulsYDbHdv393qBhx6iFUy7naIMu3ePbRsYRu906gS0aEF9zvr18v12uxzV2RguF+1X0+dE+AEMHqztBSGjE9FQfPny5Rg3blyDj48fPx7LxJWKiStef53qZ5vNwJNPUipAYwweLA8anniCVou1QjyKAB07Uv6k00n+DXWxWKhzD7VCQGEhXUC0ytGjtHoFUC1pvVQxYNTjwgtpgLNnD5kEhsuZM3IVErECw2iTfv2ofywtJfPQhnA4qI8MNmKqoqLx8oA//URiqpZL6gIUht+0KZ0Xok+NBT4f8PTTFC1hNpM3Qf/+sXt/hjEKkuS/SkAoFQJEilPNSAAuDagNIhIBHA4H1jQUKwxg9erVcOjBApxRlK+/Bv71L9q+5x45rDwY/v53CtvbuRP47LPotC9U3G55YB8PlQEEklS7SoA/TKbQRACXiwbNWhYBXniBjvnw4drLtWW0SWqqHB7+6afhv87KlTSB6d6d85a1jskETJpE24HSQEItp1U3d9YfIgVh0iRtR1Wlp8tVVRYuJKPZWPDKKxRRKEnAI4+QzxDDMOFRUwQQ5U4tFhonBTP+EyKAiAQoKZGNtlkEUJeILh/XXHMN3nvvPfztb3/D3r17q70C9u7di7/+9a/4z3/+g2uuuUaptjI6YONGuugClOcvwmSDJSMDuPVW2l6wgFbG1GbfPurEmjaNP4d40UE3JALYbJQXFiwVFTQg1qo2uGED8P33tHo0fbrarWH0hEgbWbIkvKoZAKcC6A0Rir9qFU3e/RFKxJTPR7+dQCJpSQm9HwCcd15o7VWDiy+mVfjKSlqRjzRdpjHefRd4803avvdebVVOYBg9Iip85OfXTtUNdhFIlAcUguX69SQmtG/PYrfaRCQCPPXUU7j66qvx0ksvoXv37rDb7bDb7ejevTsWLFiAq666Ck899ZRSbWU0zqFDwF13kTo4YQLV5A2Hyy8HunWjwc7zzyvaxLAQ4bm9e2t71SUaDBlCE+KDB/2v4tjtdJyCTd2oqKABsRZrn3s8wLPP0vall1I6BMMES04O/WacToqGCpWKCjnnkksD6oOuXemYV1WRN0BDhDJYrhs2W5fly6kP7dgR6NIl9DbHGpOJSmdarSRefPdd9N7r88/Jfwig8Qf7uTBM5NhsNBYEaqcE2O3BLdTVTYUSqQBcFUB9IprS2Gw2vPPOO9iyZQsef/xx3Hjjjbjxxhvx+OOPY/PmzXjvvfdg0+Jon1GcM2co7K+4mELmH3oo/AmzxUKOvpJEvgIbNyra1JARfgDxUBqwLk2byp/bXzSACHUNNiUgVP+AWPLll8Cvv1L1h7/8Re3WMHpDkuRJxyefhL7iuW4dDZZatgQ6d1a+fYzySJK80hwoJcBqDc4c0OlsXCQV7zNlSuPeAVqhfXvghhtoe9688CNlAvHdd+QlBFAUong/hmEiR3jU/PijfJ/dThFQLlfg55aVyd5KPp8sAghhgVGPsEWA8vJyXHrppXjvvffQp08f3HvvvXjllVfwyiuv4N5770VOTo6S7WQ0TGUlMHMm1QRu1Qp45pnIw7179wZ+9zvafuqpxjuZaCJEgHjyA6hJIF8Am41WroLNdy0u1qbRXlmZXJryxhspx5thQuX882lgdOAAsHVraM8VqQBjxuhncsfIIsCGDRQu6w+Hg/q+xiKmhAjQUCTA6dP0PjXfVy9Mm0bpdAUFwIsvKvvaa9YA999PIca/+x3w178q+/oME++MGEHXpT17gFOn6L5g/U5qlgc8coSiSq1WSjNg1CVsESAxMRHfffcdyrW8tMdEHa8XmD2bJsrJyRS+n5amzGvfcQe91oEDwH/+o8xrhsqZM8CxY7Qdj5EAgGzsuH59/TJPkkTKbrD5roWF2vQDeOstGpy2aQNceaXarWH0StOm8uTsk0+Cf57bLYdZciqAvmjdmgRirxf49lv/+9jtwZVTFbmzDbFkCT3epw8J7nrCZqO0AIDMM7dsUeZ1t26V0xAnTiQfABbRGEZZmjWTq2OJa5XN1rjficdDj4voJrGY1LcvkJAQteYyQRJROsDIkSMDVgdgjM+LL1KOotVKYX7t2yv32snJsrPwa6/Fzlm4JsLBtEMHGuDHI92708p4WZkcFVETq5VWuRqjspL+tFYZ4MQJ4L33aPvOOxt35maYQAgz1GXLgq8P/8svQFERkJJCgyNGXzSWEiBEgMbE0oqKwGl0S5bQrR4MAf0xYAAZBQIUuh9phN+vv1KfXVlJYvUjj3DNcYaJFv5KBQKBxc26KU7r1tEtVwXQBhGJAC+99BJWrlyJ+++/H8fEcikTN3zyCfDOO7Q9e3Z06vBecAHVQa6spDSDWCMmvUIBjUdMJrnDXr26/uN2O1UIaCwHurxcmyLAiy9SSsPAgbwKy0ROr15kbFpVBfzvf8E9R6QCjBypzXQZJjATJ9Lkc+dO4PDh+o+LlelQwmbrcugQsGsXvc+ECRE1V1XuvFOO8Hv77fBf58gRihYsLSXhbO5cFnAZJpoIEeDnn2ksBzTud1JZKZudulxyOhOLANogIhEgJycHx44dw5w5c9CuXTvY7XYkJyfX+ktJSVGqrYyG+OknuugCwC23RG9lQpIovM9spoFyXQUy2ojKAPHqByAI5Asg8sLERaEhKipIKNDSSs2WLcDSpfQ7mzGDw0iZyJEkORrg008bF8d8PuCHH2ibRSh90qyZbHLVUDSAxULRHg3h89GEtiFTQPG6Q4cql3KnBikp5CEEAG+84V80aYy8POD22yldr2tXYP58baaZMYyR6NQJaNGCVvfFir7DQf2a1+v/OU4nPWY2U+pORQWQnq6PyibxQERrDpdddhkkHjXHHb/+Su79Hg9w0UXAn/8c3ffr1Am45hpaNXj6aWDQoNhc8N1uWtkB4tcPQCBU2927KXc+PV1+TJSJKS8PnONVVqatSbbXK5cEvOQSGkwyjBJMmUL+KEeOUHWTgQMb3nf/fjJVtdl4dUTPTJlCkVJLlgA331y/r7PbacXM5/PfD7pctGLmTwTw+WQRQK+pADWZPJmiZNauBebMIVPWYK8NhYUUAXDyJNC2LfDSS/GbqscwsUSSKBrgww9pQW7MGBqLl5TQ5D4pqf5zapYHFMLB4MHxV25bq0QkArz11lsKNYPRC/n5Dtx3nwXl5TQZv+++2EzsbryRBlcnTtDqwe23R/899++XO7Z4rxmfnk7eALt308Dtggvkx8xmGqQ2Fup69qy2UgEWLyaRJykJuPVWtVvDGImkJJoUfvoppU0FEgFEKsCQIUBiYkyax0SBMWOofztyhPqVXr1qP+5wyClR/sRSp5NEAH8T2h07yKDW4ZBLdekZEeF35ZUUHvzVV8CFFzb+vNJScv4/eBDIygJefpmiMBiGiQ2jR8sigNdLoqUwPfUnApSXyxN+EUkqIksZ9WEthgmasjLgsceG4tQpCR07xjYHLzGRHIAB8iE4eDD67yn8AHr3ZtUSkDtuUeO1JpJEA7SGcLno96N2yKbHQ4POL7+UowCmTasd2cAwSiBSAr7/niJlGkLUXeZUAH2TlCQfQ38pAY1VCKispH7SnyeEeL2xY40jFLVuDdx0E23Pn9+4iWZlJaVs7dpF6RALFgDZ2VFvJsMwNejfn/qgggJaFDKZAleIKikhoeDsWdofoEgARhsoYkF07NgxbN68GUVFRfD6SQy57rrrlHgbRkXcbuCf/zTj0KEUNGvmw3PPSTEPwRszhkKRVq4EnnoqtBDCcGBTwNoMGwb8+9+k5nq9tYURhyPwIK6iggZxaq7aLF9OFSxEjVuAPkOLFuq1iTEu3bvTavCOHcAXXwA33FB/n7w8WjUWYZaMvpkyhcoEfvstMH16bf8Ts5n6zfJy//1gVRXd1r2mud1y6cEpU6LSbNX44x8pwm/vXhICHn7Y/35uN6UgbtpEYsuLLypbiYhhmOAQaWvLl9NYvGdPGkeVlNTfV/R3Vqu8eNS1K5CREds2Mw0TkQhQWVmJ66+/Hp988gm8Xi8kSYLvNxekml4BLALoG5+PJk+rV5tgs7nx7LNAy5axt7CWJOAf/6B69Rs20OpINPMjWQSoTZ8+NAArLCRFt2dP+TG7nSIBGsppraioXSYm1ixfDtx9d/37vV7gn/+ki9S4cbFvF2NsLruMRIDPPgOuu65+RJGIAjjnHI5GMQLDhpHxXUEBXaOEWaBAkigiyh+Vlf5F7Z9/pkiS1FTjeUZYLNT/TptGKQHnn1//O/N6gYceogmH3U5iQffuqjSXYRhQSsDy5XT9+stfaBFIVIiq2YeJFKfERFkEMFofpnciCnK+77778Omnn+Lxxx/HihUr4PP5sGjRInz77bc477zzkJOTg61btyrVVkYl3nsP+PhjQJJ8mDFjI3r2bMTuOoq0aiUbET73nH/1UQkKCym3E2BTQIHFIodx1S0V6HAEDnUtL2/cJT1aeDwkYgXimWdoP4ZRkkmTgCZNyPhv/fr6j3NVAGNhtQLjx9O2v5QAMVj2R1lZ4FSAiRONWT6yd2/giitoe84c+h6EyL9hA6UdLl5MkRRPPRWdUsQMwwTPiBE02d+zh6LZhN9JTRNAgP53uWpHArAIoC0iEgE+/vhjTJs2Dffccw96/eaC06pVK0yYMAH/+9//kJqaigULFijSUEYdli2jyTYATJ/uxdChuaq2B6AQwnbtaLXl5Zej8x7bt9Ntu3a0ssMQDZUKtFpJ8W0oL6y4WL0ogM2ba6cA+CMvj/ZjGCVxOGQTzU8+qf1YaalcM5lFAOMgotOWL69fNtVup0muy1X/eaWl9Y1TKyvJUwIgR32jctttQGYmmR+edx6VHb7/frr9+GPa55FHgJEj1W0nwzDkySEiZEWEjr9FIKeTUnkOHwby82m/vn1j3lwmABGJAKdOncLg35YGE36zuy2rEet22WWX4dNPP43kLRgV2bYNmD2btn//e+CqqxooBBpjbDZyFgZogLBrl/Lv8csvdNunj/KvrWeECLB9e/0oDEnyHwng81FkhVqVAfLzld2PYUJBGAT++CNw+rR8/08/0QCpXTvObzYSOTnkXF9WBqxaVfsxh4Mm9v7EUqezvtHuypW0b8uW9LpGpUkT2e+gISE5VibEDMM0jvCwWbmSIpQ8nvrnrtNJ4z+xaDRggHqLQYx/IhIBsrKyUFBQAABITExEWloa9uzZU/14cXExKutK4YwuOHaMnHidTjrZZ87UVo33QYNo0ODzUQih0qHc7AfgnxYtaMLi8dQPb7Za/ZsDClNAtUSAmuZcgWCzGiYadOpEqx8eD/Df/8r3i1SAsWPVaBUTLUwmeUK7ZEntx6xWigLwJ5b681P55hu6nTxZW9dfpfF4/KdP1IRTthhGOwgR4OefqT/z53dSUUH9oUgF4NKA2iMiEWDIkCFYVUPqvuiii/D000/jvffewzvvvIP58+djaJQTQBYsWID27dvD4XBgyJAhWO8v8bIGH330Ebp37w6Hw4FzzjkHX3/9da3HfT4fZs+ejRYtWiAhIQETJkzA3r17o/kRNEdxMTkbnz0LdOsGPP548BOpWDJ9OhnV7dxJxltK4fGQmRfAIoA/GkoJcDiAoiIycqqJmiLA9u3A0083vl9WFtCvX/Tbw8QnIhrgs8+of3G5KBIA4FQAIyJEgFWr6HpaE0nyv9rt8dRe7S4qkr1XjFYVoC6cssUw+qJTJ4pQqqqiBSG7vb7fSUkJjQfFect+ANojIhHgb3/7Gzp27Ajnb24Qjz76KFJTU3Httdfi+uuvR0pKCl544QVFGuqPDz74ADNmzMCDDz6ITZs2IScnB5MnT8apBq4mq1evxh/+8Af8+c9/xubNmzF16lRMnToV20UCOIC5c+fihRdewMKFC7Fu3TokJSVh8uTJcRPR4HIBd90FHDpEE6PnntNuXeKMDMolBICXXiKPACU4cIAGaUlJQMeOyrymkRg+nG7XrKlt9teQOWBFBe0XayFpyRJyrj1zpvF60jNnalPoYozB+PHkLZKXRxO7jRtp1SQ9nY1HjUiXLjRIdrnIG6AmFgtN8OtS11l72TJKF+nalV7LyHDKFsPoi5plbYUvQGmp7Hfi89H/e/aQUJCVxWlvWiQiEWDkyJF4/vnnYf9tia9NmzbYtWsXNm/ejF9++QW7du1Ct27dFGmoP5599lncdNNNmDZtGnr27ImFCxciMTERb775pt/9n3/+eUyZMgV33XUXevTogUcffRT9+/fHSy+9BICiAJ577jncf//9uOSSS9CnTx+8/fbbOHHiBD7//POofQ6t4PMBjz5KA9SkJOD554HmzdVuVWAuv5zKBZWWUnuVQPgB9OrFE0N/9OtHHX5eHgkmAmEOU3eVq7Q0tqGsXi+wcCGVnhLpLB98QC7TmZm1983Kovu5PCATTex24MILafvNN4F33qHtUaPqlw1kjIFYva8b5u5wUNpU3Yipun2keJ6RDQEFwaZiccoWw2iHmiKAzUYRn2IRqKqK/jZtov+HDjV2SpNeUbzgjMlkQk4MHGyqqqqwceNGzJo1q9Z7T5gwAWvqxin/xpo1azBjxoxa902ePLl6gn/w4EHk5uZiwoQJ1Y+npKRgyJAhWLNmDa666iq/r+t0OqujIQDyQgAAl8sFlz8bYI3gdtPE3+ulv1dfNeHrr80wm32YM8eDjh19tQYqXq+r1q0WkCTg3nslTJtmxtdfS7joIjcGDIisFt22bWYAJvTq5YG37kjNgIR6XG02oH9/M9asMWH1ag86dJC/I6H+pqbK+585Q8+JxVdZUQE8/LAZy5fTzOraaz247TYvzGbKvR41CtiyRUJ+Pg0o+/b1wWyOTdtiiRbP1XinRQsJgKXabwQAvv/eh2HDPDj33OD6LD6u+mHiRGDBAis2bvQhN9ddLUDabNRHlpRQlJ0YI5hMrup+KC8P2LSJcgMmTXIZrn+qS04OkJlp+S0lwN9MwYesLCAnx62b74LPVePBx7Q2/foBiYkWFBRI2LfPjZQUH4qLgYQE6uOqqoANGywAJAwZ4obXq1558UCEelxFSp+WCXbuGZEI0LJlS4waNar6LxaTf0F+fj48Hg+ysrJq3Z+VlYXdu3f7fU5ubq7f/XNzc6sfF/c1tI8/5syZg4cffrje/d9++y0SNRpL7/EAO3em4+xZB9LSKnHqVCJef50K8N5yyxa0bXsEJ074f25u7tIYtrRxUlOByZP7YPHiDnj88QrMn/89rNbwO5vNm8cBaIpWrX7GiRN5irVT64RyXHv06Ig1a87BihUFOPdcWXSTJIqkENEUNWno96QU+fkOPPHEEBw4kAqLxYtbb92C8eOPIq/OIWzRgv4A1HvMaGjtXI1X1qxpgXnzBgHwoeYkp6gIuOceM+6552cMG3Yy6Nfj46oPevQYiV270vHxx3swder+Wo+tWFF7X693aXUf+emnnQH0Qq9e+fB4fop636kFpk1rgaeeqn+O0P/ADTf8jLy84M8RrcDnqvHgYyqTkzMIa9a0xNdf78fVV+/Gxo3yY/n5Dhw8OBkmkw9t2nyLEye0PXMO5rhKErB1K/1pmfKGyqzUISIR4JJLLsGqVavw8W+FXJOTkzF8+HCMHj0ao0aNwqBBg2CNg7ous2bNqhVhUFxcjDZt2mDSpElITk5WsWX++ewzCTNmmHH8eP0L7fXXe3D99b0B1E9U9XpdyM1diuzsiTCZtHVc//EPYP16H44da4oVKy7A9deHt1xQVAQcP06fbfToAbVWtI1KOMd1yhQKa965sznS0s7HbxVCceYMqcAi9KukhHKg09KiWxpmxw4J995rRn6+hNRUH+bO9aJv33MAxKezo5bP1XjD4wH+/W9xqa27yikB8OGttwZh6lR3o+lHfFz1xUUXmbBrF7B2bS/cdpucGnn8OK1+t24NlJe7sGLFUiQnT0STJnRMV6+m38vFF6eiZcvzVWl7rLnsMqBZMw+eecZcyyQwKwuYMcODc8/tB0A/7q18rhoPPqb1mThRwpo1wJYtXXHttR3RogWV1j5+HHjmGbre9ezpQ7duE1VuacOEclyPH6dqP61axaZt4VJc15G2ASISAV555RUAwNmzZ7Fy5UqsXLkSq1atwuzZs+F2u2G32zFkyBB8//33kbyNXzIyMmA2m5FXZykvLy8P2Q24gGVnZwfcX9zm5eWhhVgq/O3/vn37NtgWu91e7YtQE6vVqjkR5NNPgauuqm3oRoiT1QyTKfBI1GSyaq4DTE0F7rwTePBB4PXXzZg82YyWLUN/nZ076bZtW6BZM219xmgTynHt0IFW00+elLB5sxUjR9L9CQmUF+b1Uu5rVRWFTTkc0Wv3kiXAI49Q/n+nTsD8+RJatlQ800mXaPFcjTc2bWrM+VxCXh6wdasVAwcG95p8XPXBpElU2m73bglHjlirjbFMJvJOsVrlVCSbjY7pvn3Avn1kIDhhgiWuPCPGj6e0rc2bUZ2y1a+fBLNZv/05n6vGg4+pzKhRtODz668SSkqsaNKEvLRcLmDLFtpn6FATTDroyII9rmZz7UouWiTYuaciRyUtLQ0XX3wxnn76aXz44Yd48cUX0aVLF1RWVuLHH39U4i3qYbPZMGDAACxbtqz6Pq/Xi2XLlmFYA8Uohw0bVmt/AFi6dGn1/h06dEB2dnatfYqLi7Fu3boGX1NPeDw0Ua4vAMg8+6x+a/Gefz7Qvz9NBufNC+81RL4ulwYMjCT5LxUozAGFOYyoHxsNvF7gX/+qbQD45psIS/xhmGjBzufxS2qq3E/WNAh0OORyWsJOSIzZliyh2+HDqaJEvGE2AwMHUrTZwIFszsswWiYtTR4vb9womwMWFckh81waULtELALs2rULr776Kq699lq0b98e7du3x7333otOnTphzpw5WLlypRLt9MuMGTPw2muvYdGiRdi1axduvfVWlJWVYdq0aQCA6667rpZx4J133onFixfjmWeewe7du/HQQw9hw4YNuOOOOwAAkiRh+vTpeOyxx/DFF19g27ZtuO6669CyZUtMnTo1ap8jVqxcCRw7FngfPdfiJZNAGjT8+CPwww+hvwaLAMFTs1SgwGIhw0mRjlRcHB3FtLISmDULeO01+v/aa0n4SUpS/r0YJhLY+Ty+EVUCvvlGFuDtdhooO52yCCBJ9LgQC847L/ZtZRiGCZXRo+l27Vq5QtTGjZQOmpTEZXC1TEQxVs2bN8eZM2eQmZmJUaNGYebMmdUGgVIMakFceeWVOH36NGbPno3c3Fz07dsXixcvrjb2O3LkSK0QlOHDh+M///kP7r//ftx3333o0qULPv/8c/Su8Qu9++67UVZWhptvvhmFhYUYOXIkFi9eDEc045ljxMkgPXX0vCLVsSPwxz8CixbRpHDwYFTnqzeG1wts307bLAI0jlilOXKExKXWrel+Eerq81EpLD+ZMhFx6hQwcyawaxeJDvfdB1x8sbLvwTBK0a8flaYMlBKQlUX7McZj9Gha+T9+HNixgwbEDgcJpOXlsggA0MrZyZNUNUCU32IYhtEyo0YBL70E/PyzHAWwdi09NngwjdMYbRJRJEBBQQEkSUL37t3Ro0cP9OjRA126dImJACC44447cPjwYTidTqxbtw5DhgypfmzFihV46623au1/xRVXYM+ePXA6ndi+fTvOP7+26Y4kSXjkkUeQm5uLyspKfPfdd+jatWssPkrUqWFzEBC9r0jdeKPIVwfeeCP45x04AJSVkWjQqVP02mcUmjQhcytA7vABMgA8c4YuBpWVyvoB7NgBXHcdCQCpqcArr7AAwGgbs5mMSwMxcyaHPRuVxETKcwcoGgCoHTFVVibvK1IBzj03uj4qDMMwStGxIxnlVVVRNO2ZM8CGDfRYjSkZo0EiEgFOnz6NTz75BAMGDMDixYtx/vnnIy0tDYMHD8bMmTPx+eefI1/Py8oGY9QoWq0NpNEYYUUqIUEedL/zDk3ug0FEAfTsycplsIh819Wr5fscDrkOdmWlclUBvv0WuPlmilTp2JGiPfT+W2Xig3HjgLlzUV0rXpCVRfePG6dOu5jYIFICli6lyT8gR0yVlND/bjc9DnAqAGN8AnlTMfpCkuTIpU2bgIICWqgB5DEio00imuqkp6fjkksuwSWXXAKA6hKuWbMGK1euxIcffojnnnsOkiTBLa56jKqYzcDzzwOXXy7nH9bFKCtSY8ZQp7RyJfDUU8DChY0b1Ina9n36RL99RmHYMGDBAlJ9XS7K/3c4SAkuKKDfWKS/J6+Xcv9F/v/IkcBjj1EkAsPohXHjqF+q7XxujP6WCczQoWTyd+YMhcwOG0biaH6+nA6wdq2EwkKgWTMEXSWCYfSCxyNHvrhcdF1v2pR+74z+GTkSeP99YP16YN06Ot5t2mi/lF68o1jNhr179+L999/HO++8g3feeQe7d++Gz+dDht5jyw3GpZcCH39c/8Q04orUXXdRPvrGjXIYZiDYFDB0unYF0tPp4i6cYG02usiXlUVeGaCyknL+hQDwxz9SyS0WABg9ws7n8YnFAkz8rUy2MP5zOKjfrKqi/5csoeHYpEkcicboH5+Pft+nT5Nv0MmTFO3SogVVcRJC17Fj+q1IxcgMGECpT2fOUNUmgFMB9EBEl5qXXnoJP/74I1atWoW8vDz4fD506NABo0aNwn333YdRo0YZJp/eSFx6KXDJJTQY+eEHoH17Yw5IW7Ykf4AFC4DnniOlMjnZ/74lJcDBg7TNTqbBYzJRR//111QlQFzYfT5a4Yokr7WuAeCsWfS7ZRiG0RtTppAA//331JfZ7WSc6nYDFRVm/PCDVL0fw+iRykqa+Atj4IQE8u7p2pXGXk2b1jYKTk6m6/uxY7QQxT4Y+mXlSlnMyc2l26VLyRjQSIuLRiMiEWD69Ono3bs3LrvsMowaNQqjRo1Ci2Dd5xhVMZvlHJ70dOMJAII//hH46ivg0CHg5ZephKA/hB9A69YcnhYqw4fLIsBf/0r32WzkCxDuRX3nTmDGDAqXTUkBnn6aVg8YhmH0SJ8+smHtypXAhAlyFMD69dmorJTQujXQq5e67WSYYHG76TpfXk7bdjtF6bVpQ5P/5GRaHW6I1FS6rv/6K3k3JSVR3XlGXyxfDtx9d/37i4rofqNFGRuJiESAgoICpKSkKNUWhlEcq5Um/rfcAnzyCXDRRf4HWcIPgFMBQmfIEAr7//VXCv1r3pwGAwUFNIEPlaVLgYceokiCjh2BZ5+Vyw8yDMPoEZOJVvn//W+KwhPpAQDw44/UwZ13XuQpVAwTLTweqvpTWkoClsVCE/f27WnynpxMIoAphERju53GZCkpFBVw4gSQnR3aazDq4fFQOe5APPMM+eEYdbFRz0R0mtUUAE6ePImtW7eirGa9G4bRAAMH0uDK5wOefNJ//pmIBGARIHTS0oAePWhblApMTibjs1AqA/h8lEs2axYJACNGAG++yQIAwzDGYPJkuv3pJ1oly8igyc7mzZm1HmcYLeDz0aQ/Px84epSiWKqqKHS/f39KsRw9msZNrVvTdT+cybvJBLRtCwwaRJGYR49SagGjfTZvptTNQOTl0X6M9ohYa/vvf/+L7t27o3Xr1ujfvz/WrVsHAMjPz0e/fv3w+eefR/oWDBMx06eTQr1rF0UE1MTrlU0BuTJAeAwfTreiVKDZTKF+wVLXAPCaaygCgA0AGYYxCp07A126UOj08uWUM/3TTyZ4vSZ07+5F+/Zqt5CJV4SPT3ExTeqOHaO/sjLK5e/dm67zo0fTwkq7drQAoKSJZbNmZDDXuTMJD4WFyr02Ex2CrQLP1eK1SUQiwJdffolLL70UGRkZePDBB+GrUXMuIyMDrVq1wr///e+IG8kwkZKeDtx+O20vWFC7Qzp0iMLb7Ha6+DChM3Qo3a5fH7rT76lTwM03UxqAxQLcfz/w979z6BjDMMZDrPaLijWLFwtDQC6czsQGr5dW+M+eJRO3o0dpwl9YSI+lpwM9e1Kq38iRVNKyc2c51S+aOBwkOOTkUJWhkyepTYw2CbYAHBeK0yYRiQCPPPIIRo8ejVWrVuF2McOqwbBhw7CZY0AYjXDppXRhKysDnn9evl9EAfTqxaWZwqV3b1q1LyqiaItg2bkTuP56uk1JIfPGqVOj1kyGYRhVEe7/mzYBCxcCv/xiAuDDxIk802GUR5j35efTRP/oUcq7Lysjz6TWrWnCPWwYTfjHjCFH9y5dyMgyKSn2PhUmE/kMDBpE44KjRylKgdEe/foBmZmB98nKov0Y7RGRCLB9+3b8/ve/b/DxrKwsnGosWYRhYoTZTCaBkkSrMD//TPcLEYD9AMLHYpFrwoqUgMZYuhS46SYyE+zYEVi0iCsAMAxjbHbupMkXALz+Ot1arV5s386OgEz4+HyUVldURDnYYsJ/+jStqDdtShP7AQPIb2fUKPrLyQE6dKCJWnKythZC0tMp9aBjR/pMxcVqt4ipi9kM/OMfgfeZOZMjO7VKRKd7YmJiQCPAAwcOID09PZK3YBhF6dkTuPxy4KOPgDlzgHvukSetXJopMoYNA5Yto1KBN9/c8H4+H+X+v/oq/T98OPDEE5z/zzB6xOulFCCPh1YdPR75PrdbvXZJEq0iaqn2eEOltFwuE+65h0tpaQ2fj0Lm3W6axDT0F+uVco+HVsYrK+lWnGd2O/1lZpInT1IS+U4kJoZm0qslEhJogSY5Gdizh8oRZmXFZxUNt5tSNsQx1cp3MG4c9V3z5tU2CczKIgEgVn1aRQVFuDRrxtUlgiUiEeDcc8/FokWLMH369HqP5ebm4rXXXsOFF14YyVswjOLcdhtFAhw5IvsEANSJATwIC5dhw+h2xw5ajfBXHrCyEnj4YYoCAICrrwbuvJNVYobRAmIy39Cf200TI4Ek0V/dSZHNRpMRm029wVhVFa0e+nw0KVJ7hTNwKS0JgI9LaWmIykqa0KSm0qTC6aTflJiA1xS7/J0T4ndfWEi/PZOJbsU5Eux54XJRW8SE3+ul59vtNBnMyqJrrZgYJiQY7/djNlM0QNOmFElz9CiVEdSrsBEO5eVyCebyckrvcDi0I3SOG0d91+bN1LaMDEoBiMVvsbwcOHNGLll57BjQqpXxzoNoENFl8fHHH8fQoUMxaNAgXHHFFZAkCUuWLMHy5cvxr3/9C16vFw8++KBSbWUYRfj5Z8qRq0t+Pq3S8GpMeGRl0YX6wAEyCKxZBxugC9jMmXQRN5upFCDn/zNM7CgpodUSMan3+eTVJJ+PJiZmc+3JSkKCPKF3OCiU3WJp+E88T218PurTDxwg8zO7ncKL1RIlGi+lJVWX0ho4MLpt8Xrpt5CUpL44ojV8PqCggCbdHTtSCH1iovyYiHZxu2v/1bzP6aTz7NgxOl+8XhIQxLknVu7rigdCLPB6afIP0PMdDhIj0tLomInJvsOhndXgWNC8OfkE7NlDhs5paSQMGB3xe+zZE+jUiX5LZ84Ax4/TbV4e/S6Sk9UVRszm6PddNSktpUgdu51SWtq0oc+/bZssBHD/FpiIvp5u3bph1apVuPPOO/HAAw/A5/Ph6aefBgCMHTsWL7/8Mtq1a6dIQxlGCQKvxhC8GhM+w4fToPuLL+i7Fmrwr78CM2aQEJCSQkLLgAFqt5Zh4oeCApp8NG9Okwe7PfCEXogBeg2rlCT6rM2akQiwbx+tIKak0F+sJ09aKaUlVhRTUuh7sdlIHOHrnbz6n5ZGIegtWtT+nUgSnTPC0yEQLhdNREaMoHPIn1hQ8z6x4u900nmXkiJP9hMTg3vPeCAxkUo5N20qpwdkZhpTDPF46BxNSCC/pFat5N9gUhKZOhYXy4KA6OObNKHvx6gT4JISmvwnJpJI17p17cjTnBzqz44eBVq25HMnEBH/RHr16oXvvvsOZ8+exb59++D1etGxY0ekpKTgrbfewsUXX4xff/1VibYyTMQ0vhqDmK3GGJGEBLpds4b+AOqcy8ro4tSxI/Dss9RpMwwTG/LzaXWxb18aFMUTZjMNnps3p0nZgQOUCpaeHlsfErVLafl8NPl3u4Fu3WjlrLCQvo/jx2lAHa+5tCJqpKqKVlo7d5ZX/5VARMdEu7xevGA20zFKTqbIwmPHKD3ASJM9p5MEgOxsigBITa2/j/A9SUkB2rWj87mggKo/5ObSPk2b0p/ez2ufjwSPwkLqt3v0oGtZcnL9fRMSSCgymYDDh0nMi6fUkVAISwSoqqrCF198gf379yMtLQ0XXnghWrZsiUGDBqG8vBwvvfQSnnvuOeTm5qJTp05Kt5lhwkYrqzFGZPlyMvyrS1ER3XbrBvzrX2wAyDCxRPRlffvSYChesdlIhMzKooHh4cM0oMzIiE1OrSil1bAI7UNWlhSVUlqVlSRup6dTPyyM1RITSRzJzSUx4OhRGlSnphpzZdUf4rtp1owmDnVX/xntkplJK+K7d5Ow16yZMcYXxcU0burUic7XYPonk4k+f7NmJPCdPUt9TW4uiXwmE4kFapR8jASfj76LoiLqm0SETmPH2W6nfc1m6tuysrThnaA1QhYBTpw4gbFjx2L//v3w/ZbQ5HA48OWXX8Jms+Hqq6/G8ePHMXjwYLz44ou49NJLFW80w4SL2qsxRiWYNAvhasswTGw4fZoGfDk5tKLE0CC4Z09aRTp4kCa+JhNNhqMZPitKafmrDgDQWErpUlo189s7d/a/wm21Ui5tZiZNFg4epAlVWpr/VTajUHP1v6HvhtE+SUnUvzVtSmmHFRU0ftPTRFfg85EgJUkkSLVvH94KvsVC/Vnz5vS7Fr4Bp07Rts0mm0lqFa+XxowlJdTWnBya/IdyjtpsQO/e1Kfu20ffh5Y/sxqEfMn75z//iYMHD+Luu+/GqFGjcPDgQTzyyCO4+eabkZ+fj169euHdd9/FmDFjotFehomIxldjSDGMxmqMkeE0C4bRFnl5NBjMyaE+jalNaipFR7RqBezfTyG00Q6Jb6iUVkZGBe66y4Zx45RTIUR1hKZNqc9t2TLwxMhup0iJFi0ovFqIAUZZXa1JzdV/IZDpcdLIEBYL0LVr7fSAFi30lRPvdgMnT5L41qMHjVOVwG6n76JFC9lF/+RJEsBOn6ZJcXKydlJVPB6KYhCl/vr3p/Mz3FV8i4VEX5OJRKL0dBKOGCLkU2Tp0qWYNm0a5syZU31fdnY2rrjiClxwwQX473//C5Pek08YwxJ4NYZQejUmHuA0C4bRDsLwLSdHucGkEZEk+n6EeeD+/bJ5oL8cXCWoW0orPd2NzMylaNPmfMXe4+xZcs5u146Ms0KZxCck0HNatCAR4MgRer2MDP2votX0RejShVZJ9f6ZGJnsbDk94OjR2Pt+hEtZGUXstG5NE9ZoTVITE+mvVSvqH4Sh4NmzJBomJVHfp4Z44nZTOyoqqD/u0YPEayXECbOZXs9sJjNJn08fv4tYEPKhzsvLw9ChQ2vdJ/7/05/+xAIAo3kaWo3JyiIBgMsDhg6nWTCMNhDl8HJyKPyRaRyLhQbg/swDozEgr1lKy+v14cQJZV7X7a7tJt66dfhRDU2a0ISkVSvyTzh6lCYqzZtrZ9UwFCoqSABo1oxWjXn135g0bUoRPk2bUjRLzfrxWiyJWVBAJoA9epAoFYv2SZJsGNimDeXbFxSQIJCXJ0+SmzaN/oKY203vXVVF48PevWksrrTJo8lE573FAuzYQekGRk53CpaQf24ejweOOnEZ4v+UmjUaGEbD1F2NEaXsOAIgPDjNgmHU5+RJmgDm5LDgFg52O5lx1TQPPHtWHxPfoiLKoW3Thga7Sg3HUlLIYKt1a5pUHT8ul1/Ugxu710vXeJdLzv3n1X9jY7WSoV7LlmSyV1BAf6dO0aTTZqNJbmKieq75dcv/NZauEy1MJkpBSEuTDQXz8yk96uRJalNyMn1fSrbP5ZJLGmZmUtRSZmZ0RRCTifp3k0kWAqIV8aUXwvq6Dx06hE2bNlX/X/Sb/ffevXuR6ucb7d+/f3itY5goUnM1hokMTrNQF5eLJgHl5aTisx4bX/h88oCyXz9a7WTCp0kToFcvCokXE1+zmYQVra0kut20eifSP9q1U76NkkS/qbQ0EhkOHqTfm9VK0RJa+04EFRU08UtP59X/eENMXpOTScByu0kQKCmhiJCzZ0k08/mo30xKorzzWPw+hCdFoPJ/aiD6uIwM8gc5e5bamZdHkUBWK32fkURHOZ00+ff5aNLfvj0JirEaG0oSfTaLBdi2jSJFtPL9q0FYXfcDDzyABx54oN79t912W63/fT4fJEmCx+MJr3UMw+gGTrOILWJQU1pKF7TUVFrlkiQyR2LiA5+PVmyaNKFJIAsAytGsGZ1XrVpRisCJEzQAbtZMG5PJ0lIaULdoQSuf0T72wkMhI4MmBgcPytEnaWnaEXlrrv537Uqrf7z6H99YLHIJvXbtaDIqSvGdOkXiQH6+XDozKSk60T9FRfS+nTvTOavVCCObjcZuWVkkpp05Q8Jffj71OQ4HCQLBGvZVVtLzJInEj7ZtafKvViRG27b03tu2UbvilZBFgH//+9/RaAfDMAaA0yyii9tNA/+SErnub+/etNKVkiJ/zzYbsHEjXbS57rVx8floYpqcTAJAWpraLTIeJhMNWjMy6Ls+cIDSBJo1Uy+n1OuVhdZevWhlK5ah+SYT9SvNm8uGiseOUQ5xWpq6/U3N1f9u3WgSw/0fUxe7XS6j16kTRdEJUSAvTzbLq+knEMmEVZT/M5kiK/+nBgkJJITWNBQ8cYJuT50i0SQlxX8fVFFBk2yLhaKIWrfWTgnH1q1pzLR1q9otUY+QRYDrr78+Gu1gGMYgcJqFsng8NOkvLaX/k5PJRCg9nVYp/YXiipJwTZtSKHOLFizEGA2fj45taioJAPEc0hgLLBZaPcrMpNBYUUIvIyO29eXLyymcOTOTJrlqmj8KQ8XMTIoI2L+fvpOUFPqL5UDf66XvxeOh76VTp/DLijHxhSTJE/0WLSh6pLTUv5+AyURu/qGIAm43TZqbNaPwfz0btjZpQn9t2sjfz/Hj8rknDAWdThIJbDaKvGjTRjsRVDVp0YKupT//LKdoaK2N0USjmVwMwzDxi9crD0IAurB26UKDh9TU4Ff9cnKoXNKxY6TiazV3lwkNr5dWYEWte/aAiB0OB52L2dnAoUNyCb3mzWnAGy1qlrfr3p0muVoJJRYD/aws6msOHiShpFmz2JTiEsIIr/4zSmAy+fcTKCyk8PGqKooYCMZPoKyMoiLbtaPz1ig16iVJFvvataPvRhgKigo1nTrR96f1CDUhyjgcJGbGU/QkDwkZhmE0gM8nT/y9XlLTO3WiC1RaWngTjCZNaJJos1EIc1YWr44ZgdxcmmDl5HCZI7Vo2pRc81u2lP0CrFaKDFA66kYYiWl9kutwUK5zixYkAojqCunp0YmW4NV/JhYIP4GmTUkEGDGChKfG/ATy82lFvGfP2JX/UwOzmc7x9HRKTSospDGH3sTpnBzyUzp+nPp1vaRrRIJBf5IMwzDax+ejlYLiYlptaNJEXlFLS1NmpS8hgXIQbTYK101Li83qHKM8Xi/dpqXJtbAZdUlPp+PRujWdX8eP0/mlRG68z0fhtpWVNIno0kUfBndJSbTq2bIlCQFHj1JocEaGcpN0sfqfkUECQGamNoURxnjYbPQbD+QnUFlJk+ABA9Qr/6cGVqt+0x1EZN0vv1A/3qqV8YUAFgEYhmFijBg0VFXRykHr1vLEPxqDfKuVDMTsdkoPcLs5h1xveDy02iyMpVgA0A7CKE+YB+7bR2kCYvUwHKqqaELRtCl5rOhxIpGcTNESrVtT6sTx4yRsZGSEnzohTBF9Pl79Z9SnIT+BkhI6dzlSS1+kpJAQsG2bnEZpZD8lFgEYhmFigMdDq2Hl5TRgyMqivOJmzWJjLGYy0UqiwwFs306raHpV7OMNj4cmUJmZFGJqlLxSo2G1UiRPZiaJAIcO0apgZmZoE9WzZ2ki0a4dnbN6j9xJSyPRsU0b8gs4cYJCozMyQguRFqv/zZvTZItX/xmtUdNPgNEnTZtSakBNIcCoqRwG/VgMwzDawOejQX1JCQ1ee/aMnWFWXSSJHM5tNhICTp6MPzdcvSEEgOxs+u2sWKF2i5jGSEigVeoWLWSTPJ+Pzv9App5uN/k9JCYC/fvTCrpRwlEliSb9zZrJYsDJkxSdlJ4eeLWt5up/9+6Ud8yr/wzDRIukJBICzGbqv1u2jG0Z1ljBIgCjGm435VClp6vdEoaJDsXFJACI3MAWLaLrIB4s2dnUDqF0t2xp7JA3vSJKS7VoQSkARhyEGJnkZDpurVqReeDJk3Te+Zv0FhfTX+vWJCAYdSXRZKL+p3lzEjwOHCCRKymJIgbqih7CXb15c7kkIouWDMNEG+GnZDKRt0mLFmq3SHlYBGBUweejAZEkkYEKq/qMkSgvJ0OvhATKxW/bVnuGXs2akTBRUwjgSaZ2cLtlc6I+fej343Kp3SomVMQKeFoa5fjv30/nW3Iyhci73bSfy0UrT23bGjf0tCZmM/22MzNpLHDgAK24ibJjPh+t/gPaK4nIMEx8YLeTr4nZTH2Uz6d2i5QlDi41jBY5fVouH8IiAGMUqqpo4Gqx0KC1XTttr+g1aQL060fn38GDXEJQKwgBoE0bGoDwMdE/ZjMJbcI8cP9+8g0Qjw0YEJ8eHVYrCR9ZWfSbP3hQ/l4yM+Xcf4ZhGDWw2YDevSki4OBBtVujLCwCMDGntJRy/Hr0oBWAEyfUbhHDRIbbTSGrHg+F87Zvr580F4eDLnA2G7B3L5cQVBsWAIyNzUb9Q2YmRQT4fFSxI96rddjtlOufnU3fC0AiKq/+MwyjNhYLRXU6HNqL6owEFgGYmOJyUZj0OefQxb6ignJtGEaPeL3k+F9RQStZHTrQ4F5vZl5WK4lydjuwcyedp2lparcq/nC5SBRt146EGZ4AGZfERFrldrlIBGAI8b0wDMNoCbPZeH0TiwBMzBA+AO3a0WQJkEuj+Xxs9sPoB5+PTC2FsWXPnmQao+dcXpNJzrvdsYPSGjgMN3a4XBQB0L49CwAMwzAMw0QXHQ9ZGb2Rl0dmZN27y5OlxEQKj6yqMvagt7JSNoDSGsLopKxMWSHGZKJjajTX+dJSimZJTiYjr9atjfPblSQKQ7fbyTDw+HESN/QW2VCTykq6tVq1+1usqiKBtGNHCjnUQgUJhmEYhvn/9u48Lqpy/wP4ZwaGkWFHUUBRxCUxl1yKLE1SFNRMy2upJKKmN1Mrdy2X1Fwx7WqW9bsqrnmz0sxMJRXM5GKhVprhLqkg1wXZZBbm/P44zZGJXRlm5szn/XrNC+ecM2eew3cOzvme5/k+JF9MAlCNyMkRLzBathSnAjLRaMQxNoWF8rmQ+jtBEKdCstUCcaYkgE5XvUkAU1d5o1FM+ri6ig+12j57fRQWiuP+1WoxkdWokflnWU7q1r0/c8Cff4pDd+zt/NTrxcSjSiV+3gwGsWaDibOz+FCp7v9UqWo+4aHVin8fTAkAztBARERElsYkAFmcVit2m27TpmT1YycncZaAGzfuzxYgNzqdePHbsaNtXjTq9cD33wNPP129FyBFReJUefn5QHY2cOeOmAzSasX1Li73EwO2fOGj14uzWSgU4oV/cLBjFPHy9hY/s2lpYkVc09Rdtk4QxM9afr7YS6NJk/u9jYo/7t0Tt7l3T3yeny8mCoxGcT8Kxf1EQfEkgbNz9SUKmAAgIiIia7DbJMDt27cxfvx4fPPNN1AqlRgwYAD+9a9/wb2Msta3b9/GnDlzsH//fqSnp8PPzw/9+/fH/Pnz4VXsm62ilFuUn332GQYNGmSxY5Ezo/H+l9xGjUrfxsfnfjVgOTINdXB3t80v+aaPvItL9bfP1VUcM9+wofhZMCUF8vPFLvU5OeLddb1evLAyVV51dbV+F/SiIrGNOp14JzwkRJzeyx57MTwoV1cxeefpCZw9K3ZZr1fP+rEpS2GhWMvA0xNo316ch9w0BKCsBJwgiDHW60smCgoKxMe9e+K+8/LE7Uy9Z4onCkxJAlOioKLPSWGhmPxs2lTsIWXP9SSIiIjIvtjt147o6GhkZGQgISEBer0ew4cPx+jRo7F169ZSt79+/TquX7+OZcuWoWXLlrhy5Qpee+01XL9+HV988YXZtuvXr0dUVJT03NsRbvtZyI0b4t3/Rx4pezyuRnP/S7UcabWAh4dtJgBqklIpJkJMebqQEPGCypQUyM0VEwL5+eKdXKNR/J25uorJgZoaRmC6k5yXJ352Q0LEC19bHU9uaUql+Dvw8hJnDrh6Vfx92NLwAKPxfjKpcWPxwrqy0xwqFOKxlHc8RmP5iYL8fPGivqBA7E1gMJRMFBRPEhiNYoKpWTNxVgYmAIiIiKgm2eVXjzNnzmDv3r346aef0LFjRwDAqlWr0Lt3byxbtgyBgYElXtOqVSt8+eWX0vMmTZpgwYIFeOWVV2AwGOBc7FuYt7c3/P39LX8gMpedLX7hbdmy/Hk1NRpxO51OngWxdDrbrQdgbSqV2O3clGcTBPFiypQYKG0YgVp9v7dAdV885eSI7+flJd5JDgxk8sakdu37wwMuXxYTW7aQH83PFxMAtWuL0/f4+1d/ssjUS6VWrbK3KSoqPUlgGmqQny9+hvPzxW2bNxdrSzhqcomIiIisxy6TAMnJyfD29pYSAAAQEREBpVKJlJQUvPDCC5Xaz927d+Hp6WmWAACAsWPH4tVXX0VISAhee+01DB8+vNRhAiZarRZa0xUKgJycHACAXq+HXq+vyqHVKNPdKqPx/jjY8hiNerOf5TF1nW3dWrwALu/XoFKJF3b37snzjpjBIF482OpHwfQZtZXPqrPz/fHngYHiBZNp/Lapl0Bentjtu6hIvOAzDSNQqx+sq/q9e+KdWVdX8c5sgwb3L/hs5NdSJZaKqbOz+Pvx8ADOnRPntK9b1zrDA4qK7tdqaNZM7AGgVlt3Fg4np/sJqtKYEgV6vTg8obJ/e01s7Vylh8eYyhPjKj+MqTzJMa6VPRaFINhfR+yFCxdiw4YNSEtLM1tet25dzJ07F2PGjKlwHzdv3kSHDh3wyiuvYMGCBdLy+fPno1u3btBoNNi/fz/mzJmDpUuX4o033ihzX++++y7mzp1bYvnWrVuh0WiqcGREREREREREVVdQUIAhQ4ZIN7vLYlP3XadPn44lS5aUu82ZM2ce+n1ycnLQp08ftGzZEu+++67ZulmzZkn/bteuHfLz8xEXF1duEmDGjBmYOHGi2f6DgoLQs2fPcn/51pabCxw9Cvj6Vq7Ls9GoR2ZmAvz9e0CpLPsF16+LXXPbtat89/6LF4EzZ8RCXnJiMIh3Kzt1so2u06XR6/VISEhAjx49oLLTvu/FhxEUFIizUWRni3f49XrxbrFpNgLT2O9bt8Q7s4GBYtFKHx+rHkK1qqmYarXA+fPAlSvinW1Lzx5gmqlBrRbv/AcFOdZwDTmcq2SOMZUnxlV+GFN5kmNcTT3SK2JTSYBJkyYhNja23G1CQkLg7++PrKwss+UGgwG3b9+ucCx/bm4uoqKi4OHhgR07dlQY8LCwMMyfPx9arRbqMipHqdXqUtepVCqb/kCZKlgrlVXrzqtUqspMAty+LV5oPfpo1abD8/QUL+Rster4gzIYbHtmgOJs/fNaERcX89oLRUX3hxDk5YkX/Xl5YvLLaBS7sYeEiMX/5Pa5M7F0TFUqcfYAX18xiZeZabkiiqZhIPXri93/bTWpVhPs/VylkhhTeWJc5YcxlSc5xbWyx2FTSQA/Pz/4/X0i+VJ06tQJ2dnZSE1NRYcOHQAABw8ehNFoRFhYWJmvy8nJQWRkJNRqNXbt2oVa5VV5+svJkyfh4+NTZgKA7rt3T7wL27591e+qajTiRZxeb/sXy1Vhmh6QH5+a5+QkJgVMiYFmze4XadPrxQtXOdagqGlKpTgFpIeHmAgwzR5QiT+vlaLVivUf3N3F3kUNGrCYHhEREdHDsMuvwKGhoYiKisKoUaOwZs0a6PV6jBs3DoMGDZJmBrh27Rq6d++OjRs34oknnkBOTg569uyJgoICbN68GTk5OVJ3CT8/Pzg5OeGbb77BjRs38OSTT6JWrVpISEjAwoULMXnyZGserl0wGMQv6o88In5JryqNRrxoKCyUXxKgTh353mm2Ny4u8pyBwhb4+AAdOgBnz4rDe9zcHm6IhSCIVf+1WiA4uGrT/hERERFR2ewyCQAAW7Zswbhx49C9e3colUoMGDAAK1eulNbr9XqkpaWhoKAAAHD8+HGkpKQAAJo2bWq2r0uXLiE4OBgqlQqrV6/GhAkTIAgCmjZtiuXLl2PUqFE1d2B2KjNTHFvdrNmDTc+lUolf8O/cEe8oyoVOZ/lx0kS2Qq0GWrUSu+qfOQNcuyZO2VfVO/cFBeLY/9q1xeEG/v5MpBERERFVF7tNAvj6+mLr1q1lrg8ODkbxiQ/Cw8NR0UQIUVFRiIqKqrY2OoqbN8W7fqGhD3eX1dcXyMiovnbZAkEoe7owIjlSKMSCfabhAX/+KQ4PqMx5YJr2TxDEXkUhITx/iIiIiKqb3SYByDYUFIh3u9u3f/g73m5u4pd/uTAdC+sBkCPy9haHB3h6ijMIuLqKd/bLkpsrFhatV0/sUeTn92C9ioiIiIiofEwC0AMzTX/XsqU4FOBhaTTisACDQR4F20xFDqurQBqRvXFxEf8+eHndLxro729+fhsM4nAitRpo3VqcrpF1G4iIiIgsRwaXWmQNgiB23Q8KApo0qZ47dsWLA8qhAJhpZgAmAciRKRRisVDT8IBr18S7/BoNkJ0N5OSIScTmzR+ukCARERERVQ6TAPRAbt4Uu/m2aFF91fxdXMSL/+xseSQBtNr7Ux8SOTovL3HYkGl4QFaW+O/27YH69eXR+4eIiIjIHvBrF1VZfr7Yhfexx6q/kr+vL3DjRvXu01p0OnF8MxGJXFzEAqJeXuL4/+Bgec0GQkRERGQPmASgKrtzR5wGzN+/+vft5gYYjdW/X2swGHiBQ/R3CoV4579+fWu3hIiIiMgxceZlqjRTtfsGDYDGjS3zHhqN2C3YYLDM/msa6wEQEREREZEtYRKAKi0rS/zZvLnlxu8WLw5oz4qKAKWSSQAiIiIiIrItTAJQpeTk3J8BQKOx3Puo1eKQAHtPAnBmACIiIiIiskVMAlCFdDqxYn+zZjXzfr6+8kgCqFRiIoCIiIiIiMhWMAlA5TIagcxMsYp3w4Y1857u7vZfHFCnE3s0ODlZuyVERERERET3MQlA5bpxA6hTB2jRouYuaDUa8b3suTigVitOg0ZERERERGRLmASgMmVniwUAQ0MBV9eae185FAcUBMvWTiAiIiIiInoQTAJQqQoLgdxcsQdAnTo1+961aokX0PacBABYD4CIiIiIiGwPkwBUgtEoDgMICam5OgB/Z8/FAfV6sSggZwYgIiIiIiJbwyQAlZCZCdSrBzRvLs51bw0eHvZbHFCrBVxcmAQgIiIiIiLbwyQAmblzR7yAbdnSuhexGo2YgCgqsl4bHpROJw4FcHGxdkuIiIiIiIjMMQlAksJCID9fLATo42PdtthzcUCdDvD0BBQKa7eEiIiIiIjIHJMABEC8456ZCTRtCgQFWbs1YgLAzc0+kwAGgzicgYiIiIiIyNYwCUAAgIwMIDAQaNbMNu5gKxRibwR7TAIIAusBEBERERGRbWISgHDrlnjXvWVL2xrH7uFhfzUBjEYxgcEkABERERER2SImAQgGg1gHwMvL2i0xZyoOaE+zBOh0YiJFrbZ2S4iIiIiIiEpiEsDBqdXiVICBgdZuSUn2WBzQlARgTwAiIiIiIrJFTAI4sFq1gOBgsRigLdQB+DtXV/Fhb0kANzfA2dnaLSEiIiIiIiqJSQAH5uIiJgBUKmu3pHT2WBxQqxWnByQiIiIiIrJFTAKQTfP0FGsW2IuiInEYAxERERERkS1iEoBsmr0VB+TMAEREREREZMuYBCCbZioOqNVauyUVMxjEWgBMAhARERERka1iEoBsmqur/cwQoNVyZgAiIiIiIrJtTAKQTVMqAW9v+0gC6HTilItqtbVbQkREREREVDomAcjmeXnZR3FAnQ5wd7fN6RaJiIiIiIgAJgHIDmg04oW1IFi7JeXT6zk9IBERERER2TYmAcjmaTRiF3tbLw4oCGINAyIiIiIiIlvFJADZPNMMAbZcF0AQOD0gERERERHZPiYByObZQ3FAnQ5QqVgUkIiIiIiIbBuTAGQXvLzEMfe2yjQzAHsCEBERERGRLWMSgOyCrRcH1GrFegAqlbVbQkREREREVDYmAcguaDSAi4vtFgfU6QAPD2u3goiIiIiIqHxMApBdsPXigEVFgLu7tVtBRERERERUPiYByC44OYl1AWw1CQCwHgAREREREdk+JgHIbnh722ZxQINBTFIwCUBERERERLaOSQCyGxqNtVtQOp1OrFfAJAAREREREdk6u00C3L59G9HR0fD09IS3tzdGjhyJvLy8cl8THh4OhUJh9njttdfMtklPT0efPn2g0WhQt25dTJkyBQaDwZKHQpXk5mabxQFNSQC12totISIiIiIiKp+ztRvwoKKjo5GRkYGEhATo9XoMHz4co0ePxtatW8t93ahRozBv3jzpuabY7eWioiL06dMH/v7+OHr0KDIyMhATEwOVSoWFCxda7Fioclxd7xcHtKULbp0OqFMHUNptSo2IiIiIiByFXV62nDlzBnv37sW///1vhIWFoXPnzli1ahW2bduG69evl/tajUYDf39/6eHp6Smt279/P37//Xds3rwZjz32GHr16oX58+dj9erV0Ol0lj4sqoCzM+DpaXvFAXU6sV1ERERERES2zi57AiQnJ8Pb2xsdO3aUlkVERECpVCIlJQUvvPBCma/dsmULNm/eDH9/f/Tt2xezZs2SegMkJyejdevWqFevnrR9ZGQkxowZg9OnT6Ndu3al7lOr1UJbrI96Tk4OAECv10Nvi5XsHpDpWKx5TJ6ewNWrgNFotSaUIAhizwR7DbUtxJWqF2MqT4yr/DCm8sS4yg9jKk9yjGtlj8UukwCZmZmoW7eu2TJnZ2f4+voiMzOzzNcNGTIEjRo1QmBgIH799VdMmzYNaWlp+Oqrr6T9Fk8AAJCel7ffRYsWYe7cuSWW79+/32y4gVwkJCRY9f0VCqCCDh81SqEAfvlFfNgza8eVqh9jKk+Mq/wwpvLEuMoPYypPcoprQUFBpbazqSTA9OnTsWTJknK3OXPmzAPvf/To0dK/W7dujYCAAHTv3h0XLlxAkyZNHni/M2bMwMSJE6XnOTk5CAoKQs+ePc2GG9g7vV6PhIQE9OjRAyqVyiptyM0Fjh4FfHzEYnzWptMBd+4ATz0FeHhYuzUPxhbiStWLMZUnxlV+GFN5YlzlhzGVJznG1dQjvSI2lQSYNGkSYmNjy90mJCQE/v7+yMrKMltuMBhw+/Zt+Pv7V/r9wsLCAADnz59HkyZN4O/vj2PHjpltc+PGDQAod79qtRrqUirVqVQq2XygirPmcXl4iAUCdTrbmJLPYBDb4e4O2Huo5fp5dWSMqTwxrvLDmMoT4yo/jKk8ySmulT0Om0oC+Pn5wc/Pr8LtOnXqhOzsbKSmpqJDhw4AgIMHD8JoNEoX9pVx8uRJAEBAQIC03wULFiArK0sabpCQkABPT0+0bNmyikdDlqBSiYmAW7dsoxifVgtoNLbRK4GIiIiIiKgidjk7QGhoKKKiojBq1CgcO3YMP/74I8aNG4dBgwYhMDAQAHDt2jW0aNFCurN/4cIFzJ8/H6mpqbh8+TJ27dqFmJgYPPPMM2jTpg0AoGfPnmjZsiWGDh2KX375Bfv27cPMmTMxduzYUu/0k3X4+oo9AWyBTme/wwCIiIiIiMjx2GUSABCr/Ldo0QLdu3dH79690blzZ3z66afSer1ej7S0NKk4gouLC77//nv07NkTLVq0wKRJkzBgwAB888030mucnJywe/duODk5oVOnTnjllVcQExODefPm1fjxUdnc3MSK/LbAYGASgIiIiIiI7IdNDQeoCl9fX2zdurXM9cHBwRCKXSkGBQUhKSmpwv02atQIe/bsqZY2kmVoNOKwAL3eNsbh20JtAiIiIiIiosqw254A5Lg0GvHCu7DQuu0oKgKUSiYBiIiIiIjIfjAJQHZHpRKr8Vs7CaDTAWo1kwBERERERGQ/mAQgu+TrK1bmtyadTkxIsGYkERERERHZCyYByC7ZQnFAnU5sh5OTddtBRERERERUWUwCkF3SaABnZ7E6v7VotYCXl/Xen4iIiIiIqKqYBCC7ZAvFAY1GwNXVeu9PRERERERUVUwCkF1ycbF+cUCFgkUBiYiIiIjIvjAJQHbLx8d6SQC9XiwKyCQAERERERHZEyYByG65u1uvOKBWK/ZGYBKAiIiIiIjsCZMAZLc0GrEyvzWKA+p04tSALi41/95EREREREQPikkAslvWLA6o0wEeHmJdACIiIiIiInvBJADZLbUacHOzThLAYAA8PWv+fYmIiIiIiB4GkwBk13x9rZMEEATWAyAiIiIiIvvDJADZNWsUBzQaOT0gERERERHZJyYByK5pNIBSCRQV1dx76nRiQUC1uubek4iIiIiIqDowCUB2zRrFAU1JAPYEICIiIiIie8MkANk1tVpMBNR0EsDNDXB2rrn3JCIiIiIiqg5MApBdUyhqvjigVsuZAYiIiIiIyD4xCUB2z8NDLNZXU4qKxN4HRERERERE9oZJALJ7Go3YI6CmigNyZgAiIiIiIrJXTAKQ3TMVB9RqLf9eBoNYC4BJACIiIiIiskdMApDdq1Wr5ooDarWcGYCIiIiIiOwXkwBk9xQKwMenZpIAer04I4Fabfn3IiIiIiIiqm5MApAseHrWTE0ArRZwdxcTD0RERERERPaGSQCSBVNxQEvPEqDXc3pAIiIiIiKyX0wCkCzUVHFAQQBcXS37HkRERERERJbCJADJgqurmASwZF0AQRB7G7AeABERERER2SsmAUgWaqI4oE4HqFScGYCIiIiIiOwXkwAkG15egMFguf3rdGIvACYBiIiIiIjIXjEJQLJhKg4oCJbZv1YrDjtQqSyzfyIiIiIiIktjEoBkQ6MR79RbqjigTgd4eFhm30RERERERDWBSQCSDVdX8WGpugBFRYC7u2X2TUREREREVBOYBCDZUCoBb2/LFgdkPQAiIiIiIrJnTAKQrHh5AXp99e/XYACcnJgEICIiIiIi+8YkAMmKRiP+rO7igDod4OLCJAAREREREdk3JgFIVixVHNCUBFCrq3e/RERERERENYlJAJIVV1fxbn111wXQ6cSigEqeMUREREREZMd4SUOy4uRkmeKAOh3g6Vm9+yQiIiIiIqppTAKQ7Hh7V39xQEG4X2+AiIiIiIjIXjEJQLJT3cUBTfthPQAiIiIiIrJ3TAKQ7Gg0YhE/na569qfXAyoVZwYgIiIiIiL7Z7dJgNu3byM6Ohqenp7w9vbGyJEjkZeXV+b2ly9fhkKhKPWxfft2abvS1m/btq0mDomqiUZTvcUBdTqxFwCTAEREREREZO+crd2ABxUdHY2MjAwkJCRAr9dj+PDhGD16NLZu3Vrq9kFBQcjIyDBb9umnnyIuLg69evUyW75+/XpERUVJz729vau9/WQ5Tk6Alxdw44b482Fptfd7FxAREREREdkzu0wCnDlzBnv37sVPP/2Ejh07AgBWrVqF3r17Y9myZQgMDCzxGicnJ/j7+5st27FjB1566SW4u7ubLff29i6xLdkXHx/g6tXq2ZdeD3h4VM++iIiIiIiIrMkukwDJycnw9vaWEgAAEBERAaVSiZSUFLzwwgsV7iM1NRUnT57E6tWrS6wbO3YsXn31VYSEhOC1117D8OHDoVAoytyXVquFVquVnufk5AAA9Ho99NVdpt6KTMdiD8fk4iIW9DMaH35fer3YE8AODvuB2FNcqXIYU3liXOWHMZUnxlV+GFN5kmNcK3ssdpkEyMzMRN26dc2WOTs7w9fXF5mZmZXax9q1axEaGoqnnnrKbPm8efPQrVs3aDQa7N+/H6+//jry8vLwxhtvlLmvRYsWYe7cuSWW79+/HxoZziuXkJBg7SZUikIBXL9ePfv5/XfxIWf2EleqPMZUnhhX+WFM5YlxlR/GVJ7kFNeCgoJKbWdTSYDp06djyZIl5W5z5syZh36fe/fuYevWrZg1a1aJdcWXtWvXDvn5+YiLiys3CTBjxgxMnDhRep6Tk4OgoCD07NkTnp6eD91eW6HX65GQkIAePXpApVJZuznlMhiAH38ElMqH68pfVCTWFujUSRxiIEf2FFeqHMZUnhhX+WFM5YlxlR/GVJ7kGFdTj/SK2FQSYNKkSYiNjS13m5CQEPj7+yMrK8tsucFgwO3btys1lv+LL75AQUEBYmJiKtw2LCwM8+fPh1arhbqMieLVanWp61QqlWw+UMXZw3GpVGJRwJs3H644oGlmADc3cZ9yZg9xpaphTOWJcZUfxlSeGFf5YUzlSU5xrexx2FQSwM/PD35+fhVu16lTJ2RnZyM1NRUdOnQAABw8eBBGoxFhYWEVvn7t2rV4/vnnK/VeJ0+ehI+PT5kJALJdPj7AtWsPtw+tVqwvwOkBiYiIiIhIDmwqCVBZoaGhiIqKwqhRo7BmzRro9XqMGzcOgwYNkmYGuHbtGrp3746NGzfiiSeekF57/vx5HD58GHv27Cmx32+++QY3btzAk08+iVq1aiEhIQELFy7E5MmTa+zYqPq4uYnFAR+GTicmE5ycqqdNRERERERE1mSXSQAA2LJlC8aNG4fu3btDqVRiwIABWLlypbRer9cjLS2tRHGEdevWoUGDBujZs2eJfapUKqxevRoTJkyAIAho2rQpli9fjlGjRln8eKj6aTTiXXy9/sG78mu1DzecgIiIiIiIyJbYbRLA19cXW7duLXN9cHAwhFJuAy9cuBALFy4s9TVRUVGIioqqtjaSdWk0Yjf+wsIHTwIYjYCra/W2i4iIiIiIyFqU1m4AkaWoVIC7O3Dv3oPvQ6FgPQAiIiIiIpIPJgFI1nx9xS79D8I0jIBJACIiIiIikgsmAUjWHqY4IGcGICIiIiIiuWESgGRNoxHv5hsMVX+tTgeo1WIigIiIiIiISA6YBCBZMxUHfJC6ADod4OEh1gUgIiIiIiKSAyYBSNZcXMTigIWFVX+twQB4elZ/m4iIiIiIiKyFSQCSvQctDigIrAdARERERETywiQAyZ6bG2A0Vu01RiOnByQiIiIiIvlhEoBkT6MBnJ2rVhxQpxOHEqjVlmsXERERERFRTWMSgGTPVBywKnUBTEkA9gQgIiIiIiI5YRKAZE+tFocEVDUJ4OYm9iAgIiIiIiKSCyYByCH4+lYtCaDVitMDEhERERERyQmTAOQQ3N2rVhzQaBR7AhAREREREckJkwDkEDQawMmpasUBWQ+AiIiIiIjkhkkAcghVKQ5oMIi1AJgEICIiIiIiuWESgBxCrVpiIqAySQCtljMDEBERERGRPDEJQA6jssUB9XoxCaBWW75NRERERERENYlJAHIYHh6VKw5omhlAobB8m4iIiIiIiGoSkwDkMDQaQKkEiorK306vBzw9a6ZNRERERERENYlJAHIYlS0OKAiAq2vNtImIiIiIiKgmMQlADqMyxQEFQRwGwHoARERERFWj0wEFBTX30OmsfcQ1Kzw8HG+99Za1m2FxBw4cQGhoKIoq6r5bhnfffRePPfZY9TaqBty8eRN169bF1atXLf5ezhZ/ByIboVAAPj7AnTtlb6PTASoVZwYgIiIiqgqdDjh2DMjLq7n3dHcHnnhCLOhcGbGxsdiwYQMAwNnZGQ0aNMDAgQMxb9481JLBl7/4+HgMHz4cAKBQKFCvXj0888wziIuLQ8OGDa3cusqbOnUqZs6cCScnJwD3j6tFixY4c+aM2bbbt2/HSy+9hEaNGuHy5csAgMmTJ2P8+PEP3Y7g4GBcuXIFn332GQYNGmS27tFHH8Xvv/+O9evXIzY29qHfCwDq1KmDmJgYzJkzB2vXrq2WfZaFPQHIoXh6ll8TQKcTewHI4P8BIiIiohpjMIgJABcXscCypR8uLuL7GQxVa2dUVBQyMjJw8eJFrFixAp988gnmzJljmV/KAxAEAYaqHlQxnp6eyMjIwLVr1/Dll18iLS0NAwcOrMYWWtaRI0dw4cIFDBgwwGy5m5sbsrKykJycbLZ87dq1JRIc7u7uqF27drW0JygoCOvXrzdb9t///heZmZlwc3Orlvcobvjw4diyZQtu375d7fsujkkAciim4oBlzRKg1Yr1AFSqmm0XERERkRyYbqZY+vGgQzfVajX8/f0RFBSE/v37IyIiAgkJCdJ6o9GIRYsWoXHjxnB1dUXbtm3x5ZdfSus7duyIZcuWSc/79+8PlUqFvL+6QFy9ehUKhQLnz58HAGzatAkdO3aEh4cH/P39MWTIEGRlZUmvT0xMhEKhwHfffYcOHTpArVbjyJEjyM/PR0xMDNzd3REQEID333+/UsenUCjg7++PgIAAPPXUUxg5ciSOHTuGnJwcaZtp06ahefPm0Gg0CAkJwaxZs6DX66X1pu70mzZtQnBwMLy8vDBo0CDk5uZK2+Tm5iI6Ohpubm4ICAjAihUrSgxX0Gq1mDx5MurXrw83NzeEhYUhMTGx3PZv27YNPXr0KNEzw9nZGUOGDMG6deukZVevXkViYiKGDBlitu3fhwPExsaif//+WLZsGQICAlC7dm2MHTvW7JjLEh0djaSkJPz555/SsnXr1iE6OhrOzuad6pcvX47WrVvDzc0NQUFBeP3116XPBQCMGDECbdq0gVarBQDodDq0a9cOMTEx0jaPPvooAgMDsWPHjgrb9jCYBCCHUlFxQJ1OzC4TERERkbydOnUKR48ehUux8QSLFi3Cxo0bsWbNGpw+fRoTJkxAbGwsTp06BQDo2rWrdCErCAJ++OEHeHt748iRIwCApKQk1K9fH02bNgUA6PV6zJ8/H7/88gt27tyJy5cvl9p9fPr06Vi8eDHOnDmDNm3aYMqUKUhKSsLXX3+N/fv3IzExEcePH6/S8WVlZWHHjh1wcnKSutYDgIeHB+Lj4/H777/jX//6F/7v//4PK1asMHvthQsXsHPnTuzevRu7d+9GUlISFi9eLK2fOHEifvzxR+zatQsJCQn44YcfSrRv3LhxSE5OxrZt2/Drr79i4MCBiIqKwrlz58ps8w8//ICOHTuWum7EiBH4/PPPUVBQAEAcJhAVFYV69epV+Ls4dOgQLly4gEOHDmHDhg2Ij4/Hxo0bK3xdvXr1EBkZKQ0jKSgowH/+8x+MGDGixLZKpRIrV67E6dOnsWHDBhw8eBBTp06V1q9cuRL5+fmYPn06AOCdd95BdnY2PvzwQ7P9PPHEE/jhhx8qbNvDYE0AciiuruKjsFBMCPxdUZE4voyIiIiI5Gf37t1wd3eHwWCAVquFUqmULsK0Wi0WLlyI77//Hp06dQIAhISE4PDhw9i3bx+mTp2K8PBwrF27FkVFRTh16hRcXFzw8ssvIzExEVFRUUhMTETXrl2l9yt+sRgSEoKVK1fi8ccfR15eHtyLfemcN28eevToAQDIy8vD2rVrsXnzZnTv3h0AsGHDBjRo0KDC47t79y7c3d0hCIJ0sfzGG2+YdV2fOXOm9O/g4GBMnjwZ27ZtM7tgNRqNiI+Ph8dfd8eGDh2KAwcOYMGCBcjNzcWGDRuwdetWqX3r169HYGCg9Pr09HSsX78e6enp0vLJkydj7969WL9+PRYuXFhq+69cuWK2n+LatWuHkJAQfPHFFxg6dCji4+OxfPlyXLx4scLfi4+PDz788EM4OTmhRYsW6NOnDw4ePIjo6OgKXztixAhMmjQJ77zzDr744gs0adKk1MKDxXtBBAcH47333sNrr72Gjz76CIA4TGHz5s3o2rUrPDw88MEHH+DQoUPw/Nvc5IGBgThx4kSF7XoY7AlADsVUHLC8GQJYD4CIiIhInp599lmcPHkSKSkpGDZsGIYPHy6NPz9//jwKCgrQo0cPuLu7S4/NmzcjMzMTANClSxfk5ubixIkTSEpKQteuXREeHi71DkhKSkJ4eLj0fqmpqejbty8aNmwIDw8PKUGQnp5u1q7id78vXLgAnU6HsLAwaZmvry8eeeSRCo/Pw8MDJ0+exM8//4z3338f7du3x4IFC8y2+c9//oOnn34a/v7+cHd3x8yZM0u0Jzg4WEoAAEBAQIA0jOHixYvQ6/V44oknpPVeXl5m7fvtt99QVFSE5s2bm/0uk5KScOHChTLbf+/evXKLNI4YMQLr169HUlIS8vPz0bt37wp/J4DYzb54b4iAgAD873//AwAsXrzYrI1//1306dMHeXl5OHz4MNatW1dqLwAA+P7779G9e3fUr18fHh4eGDp0KG7duiUlYwCgU6dOmDx5MubPn49Jkyahc+fOJfbj6upq9hpLYE8AcjienqUXkTEYACcnJgGIiIiI5MrNzU3qqr9u3Tq0bdsWa9euxciRI6Xx299++y3q168vvUav1+PHH38EAHh7e6Nt27ZITExEcnIyevTogWeeeQYvv/wyzp49i3PnzkkX+vn5+YiMjERkZCS2bNkCPz8/pKenIzIyErq/zW9YXUXmlEqldHyhoaG4cOECxowZg02bNgEAkpOTER0djblz5yIyMhJeXl7Ytm1biZoDqr8VyFIoFDCWVVSrFHl5eXByckJqaqrZxTcAsx4Qf1enTh3cKWcqr+joaEydOhXvvvsuhg4dWmJcflnKO57Ro0dj8ODB0rq/90RwdnbG0KFDMWfOHKSkpJQ6Xv/y5ct47rnnMGbMGCxYsAC+vr44cuQIRo4cCZ1OB81fXZCNRiN+/PFHODk5SXUj/u727dvw8/Or1HE9KPYEIIdTVnFAvV6sNMskABEREZH8KZVKvP3225g5cybu3buHli1bQq1WIz09HU2bNjV7FL8o69q1Kw4dOoTDhw8jPDwcvr6+CA0NxYIFCxAQEIDmzZsDAP744w/cunULixcvRpcuXdCiRQuzooBladKkCVQqFVJSUqRld+7cwdmzZ6t8jNOnT8d//vMfabz+0aNH0ahRI7zzzjvo2LEjmjVrhitXrlRpnyEhIVCpVPjpp5+kZXfv3jVrX7t27VBUVISsrKwSv0t/f/8y992uXTv8/vvvZa739fXF888/j6SkpDLvyFeVr6+vWftKSyyMGDECSUlJ6NevH3x8fEqsT01NhdFoxPvvv48nn3wSzZs3x/Xr10tsFxcXhz/++ANJSUnS0Ii/O3XqFNq1a1ctx1YWJgHI4Wg0YkXZvwpzSrRaMQnwoNVmiYiIiBydVisOu7T04+/f4x7UwIED4eTkhNWrV8PDwwOTJ0/GhAkTsGHDBly4cAHHjx/H6tWrcfDgQek14eHh2LdvH5ydndGiRQtp2ZYtW8zqATRs2BAuLi5YtWoVLl68iF27dmH+/PkVtsnd3R0jR47ElClTcPDgQZw6dQqxsbFQKqt+6RYUFIQXXngBs2fPBgA0a9YM6enp2LZtGy5cuICVK1dWuRK9h4cHhg0bhilTpuDQoUM4ffo0Ro4cCaVSCYVCAQBo3rw5oqOjERMTg6+++gqXLl3CsWPHsGjRInz77bdl7jsyMlIqsliW+Ph43Lx5U/rd14TQ0FDcvHmz1It2AGjatCn0er0U602bNmHNmjVm25w4cQKzZ8/Gv//9bzz99NNYvnw53nzzTbOaBgUFBUhNTUXPnj0tejxMApDDKV4csDidTiwK+AB/X4mIiIgcmrOz+D1KpwNycy3/MH1vq2Rv8HLa7Yxx48Zh6dKlyM/Px/z58zFr1iwsWrQIoaGhiIqKwnfffWdWgb5Lly4wGo1mF/zh4eEoKioyqwfg5+eH+Ph4bN++HS1btsTixYvNphcsT1xcHLp06YK+ffsiIiICnTt3RocOHR7oGCdMmIBvv/0Wx44dw/PPP48JEyZg3LhxeOyxx3D06FHMmjWryvtcvnw5OnXqhOeeew4RERF4+umnERoaajaef/369YiJicGkSZPwyCOPoH///vjpp5/QsGHDMvcbHR2N06dPIy0trcxtXF1dUbt27Sq3+WHVrl0brq6upa5r27Ytli9fjiVLlqBVq1bYsmULFi1aJK0vLCzEK6+8gtjYWPTt2xeAOAzh2WefxdChQ1FUVAQA+Prrr9GwYUN06dLFoseiEARBsOg7OKCcnBx4eXnh7t27Jao92jO9Xo89e/agd+/eJcbV2JtffgH+/BMICLi/7OpVoEULoBI1V2RFTnElEWMqT4yr/DCm8uTIcdXpSq+7ZCnOzmIvTktz5JhWVn5+PurXr4/3338fI0eOfKh9TZkyBTk5Ofjkk0+qqXWls8W4Pvnkk3jjjTcwZMiQB3p9Za9DWRiQHJKXF3Dpkvkyo1HsIUBEREREVefiUjMX5WR9J06cwB9//IEnnngCd+/exbx58wAA/fr1e+h9v/POO/joo49gNBofaAiEvbp58yZefPFFsyKFlsIkADmkvwp0QhDEaQNNP1kUkIiIiIioYsuWLUNaWhpcXFzQoUMH/PDDD6hTp85D79fb2xtvv/12NbTQvtSpUwdTp06tkfdiEoAckpubeMGv1Yo/9XpApWISgIiIiIioIu3atUNqaqq1m0EPyHH6VxAV4+oqXvCbigPqdOKsAEwCEBERERGRnDEJQA5JqQS8ve8nAUw9AjiOjYiIiIiI5IxJAHJYXl7iMABA/OnhYd32EBERERERWRqTAOSw3NzuFwVkEoCIiIiIiBwBkwDksDQasfu/Vis+Zz0AIiIiIiKSOyYByGGZigMWFIg1ApgEICIiIiIiuWMSgByWk5NYFyAnR+wRoFZbu0VEREREjqOoCEhMBD77TPxZVGTtFtm28PBwvPXWW9ZuBsmA3SYBFixYgKeeegoajQbe3t6Veo0gCJg9ezYCAgLg6uqKiIgInDt3zmyb27dvIzo6Gp6envD29sbIkSORl5dngSMgW+DjIw4HcHFhTwAiIiKimvLVV0BwMPDss8CQIeLP4GBxuaXExsZCoVBAoVBApVKhcePGmDp1KgpN00XZufj4eCgUCoSGhpZYt337digUCgQHB9d8w8jm2G0SQKfTYeDAgRgzZkylX7N06VKsXLkSa9asQUpKCtzc3BAZGWl24kdHR+P06dNISEjA7t27cfjwYYwePdoSh0A2QKMRL/7d3MSeAURERERkWV99BfzjH8DVq+bLr10Tl1syERAVFYWMjAxcvHgRK1aswCeffII5c+ZY7g2rSBAEGAyGB369m5sbsrKykJycbLZ87dq1aNiw4cM2j2TCbpMAc+fOxYQJE9C6detKbS8IAj744APMnDkT/fr1Q5s2bbBx40Zcv34dO3fuBACcOXMGe/fuxb///W+EhYWhc+fOWLVqFbZt24br169b8GjIWkxJAC8va7eEiIiIyD4JApCfX7lHTg7wxhvia0rbDwC8+aa4XWX2V9p+yqNWq+Hv74+goCD0798fERERSEhIkNYbjUYsWrQIjRs3hqurK9q2bYsvv/xSWt+xY0csW7ZMet6/f3+oVCqp5/DVq1ehUChw/vx5AMCmTZvQsWNHeHh4wN/fH0OGDEFWVpb0+sTERCgUCnz33Xfo0KED1Go1jhw5gvz8fMTExMDd3R0BAQF4//33K3V8zs7OGDJkCNatWyctu3r1KhITEzFkyJAS23/99ddo3749atWqhZCQEMydO9csCbF8+XK0bt0abm5uCAoKwuuvv27WSzo+Ph7e3t7Yt28fQkND4e7uLiVayHY5W7sBNeXSpUvIzMxERESEtMzLywthYWFITk7GoEGDkJycDG9vb3Ts2FHaJiIiAkqlEikpKXjhhRdK3bdWq4XWVGIeQE5ODgBAr9dDb5qIXgZMxyKnY3J2FnsBuLiI0wQ6IjnG1dExpvLEuMoPYypPjhjX/HzAx0dVLfsSBLGHQGVv0Ny5o4ebW+W2NRqNMBqNUmxOnTqFo0ePomHDhtKyRYsWYevWrfjwww/RtGlTHDlyBLGxsZg1axZ69OiBLl264NChQ3jzzTchCAJ++OEHeHt7IzExEZGRkThw4ADq16+PRo0aQa/Xo7CwEHPmzEHz5s3xv//9D1OmTMGwYcOwa9cuAJAuuKdNm4YlS5agcePG8PHxwaRJk5CUlIQvv/wSfn5+mDVrFo4fP442bdqU+dkq+quowtChQ9GjRw8sW7YMGo0Ga9euRc+ePVGnTh0A9z+bR44cQUxMDJYvX47OnTvj4sWLeP3111FUVIRZs2b9FQ8By5cvR3BwMC5duoTx48dj8uTJWLVqlfSeBQUFiIuLw/r166FUKjFs2DBMnDgRGzdurFxgrESO52plj8VhkgCZmZkAgHr16pktr1evnrQuMzMTdevWNVvv7OwMX19faZvSLFq0CHPnzi2xfP/+/dBoNA/bdJtTPFsqF9nZwG+/WbsV1iXHuDo6xlSeGFf5YUzlyZHiWljoBOA5q7z3vn37UKtW5SoKXr16FUlJSfD09ERRURH0ej2USiViYmKwZ88e6PV6LFy4ULob/scff6BOnTro0qUL9u3bh1atWkGj0SAxMRHffPMN0tPTIQgCnnzySaxfvx5FRUXYsmULmjRpgj179gAA6tatC6PRiD/++AMA8I9//AOTJ0/Gl19+CVdXV/z21xfQ5557DjqdDmlpabh37x7Wrl2LCRMmoLCwEH/++ScGDx6M77//HpcuXZL2/Xe//PIL9Ho9MjIyULt2bcyZMwfh4eFYs2YNRowYgd9//x0FBQXS62fPno3nn38ederUkdrXv39/fPjhh+jQoQMAoGnTpigoKMDvv/8urf/444/Rq1cvs/f8xz/+gRs3bgAAnnnmGXz++edlttPWyOlcLSgoqNR2NpUEmD59OpYsWVLuNmfOnEGLFi1qqEWVM2PGDEycOFF6npOTg6CgIPTs2ROenp5WbFn10uv1SEhIQI8ePaBSVU+2l6yPcZUfxlSeGFf5YUzlyRHjKgjiHfnKOHJEgb59K74E+eYbAzp3rrivv0YTCYWiUm+NL7/8Es8++yxWrVqF/Px8rFy5Es7OznjvvfcAAKdPn4ZWq8X8+fPNXqfT6RAcHIwePXrgqaeewuLFixEYGIgrV64gIiICL730EuLi4tC7d29MnjwZkyZNQu/evQEAx48fx7x58/Dbb7/hzp07MBqNAIDQ0FC0bNkSbn91Yxg9ejTq168PQLywNhgM+Oc//2k2jn/ZsmVo3LixtO+/u3nzJlQqFXr37o1Lly5h586deO45MTkzc+ZMfPTRRzhw4ID0+ldffRVnz57Fjh07pH0UFRWhsLAQ4eHh0Gg0OHDgAJYuXYq0tDTk5OTAYDCYrb958yY0Gg1Gjhxp9vv6v//7vzLbaSvkeK6aeqRXxKaSAJMmTUJsbGy524SEhDzQvv39/QEAN27cQEBAgLT8xo0beOyxx6Rtio/RAcQuOrdv35ZeXxq1Wg11KfPLqVQq2XygipPrcTk6xlV+GFN5YlzlhzGVJ0eLq4tL5bbr1Qto0EAsAljaeH6FQlzfq5dztRdtViqVcHd3l6rnx8fHo23btti4cSNGjhwpDe/99ttvpQtyQLxY/PHHH6FSqeDn54e2bdviyJEjSE5ORo8ePdCtWzdER0fj0qVLOH/+PLp16waVSoX8/Hz06dMHkZGR2LJlC/z8/JCeno7IyEgIggCVSgVnZ/FyzNvbW/q8FP9Z/DOkUCigVCrL/Fw5/fULU6lUiImJwYwZM/Dee+9h6NChcHV1NVsPAHl5eZg7dy5efPHFEvvy8PBAeno6+vfvjzFjxmDhwoXw9fXFkSNHMHLkSKn9Tk5OJdqpUqmk9fZATudqZY/DppIAfn5+8PPzs8i+GzduDH9/fxw4cEC66M/JyUFKSoo0w0CnTp2QnZ2N1NRUqQvMwYMHYTQaERYWZpF2ERERERE5Cicn4F//EmcBUCjMEwGmO/offFAzszYplUq8/fbbmDhxIoYMGYKWLVtCrVYjPT0dXbt2lbbT6/U4e/as9Lxr1644dOgQjh07hgULFsDX1xehoaFYsGABAgIC0Lx5cwDAH3/8gVu3bmHx4sUICgoCAPz8888VtqtJkyZQqVRISUmRegLcuXMHZ8+eNWtXeXx9ffH888/j888/x5o1a0rdpn379khLS0PTpk1LXZ+amgqj0Yj3338fSqVYT/7zzz+v1PuTbbPb2QHS09Nx8uRJpKeno6ioCCdPnsTJkyfNqlW2aNFC6t6iUCjw1ltv4b333sOuXbvw22+/ISYmBoGBgejfvz8AsVtOVFQURo0ahWPHjuHHH3/EuHHjMGjQIAQGBlrjMImIiIiIZOXFF4EvvgCK3WwHIPYA+OILcX1NGThwIJycnLB69Wp4eHhg8uTJmDBhAjZs2IALFy7g+PHjWL16NQ4ePCi9Jjw8HPv27YOzs7M0TDk8PBxbtmwxu0hv2LAhXFxcsGrVKly8eBG7du0qMdSgNO7u7hg5ciSmTJmCgwcP4tSpU4iNjZUuxCsrPj4eN2/eLHMo9ezZs7Fx40bMnTsXp0+fxpkzZ7Bt2zbMnDkTgFgPQK/XS+3ftGlTmQkFsi92mwSYPXs22rVrhzlz5iAvLw/t2rVDu3btzLJraWlpuHv3rvR86tSpGD9+PEaPHo3HH38ceXl52Lt3L2rVqiVts2XLFrRo0QLdu3dH79690blzZ3z66ac1emxERERERHL24ovA5cvAoUPA1q3iz0uXajYBAIhFwMeNG4elS5ciPz8f8+fPx6xZs7Bo0SLpBuF3331nVly8S5cuMBqNZhf84eHhKCoqQnh4uLTMz88P8fHx2L59O1q2bInFixebTS9Ynri4OHTp0gV9+/ZFREQEOnfuLPVUrixXV1fUrl27zPWRkZHYvXs39u/fj8cffxxPPvkkVqxYgUaNGgEA2rZti+XLl2PJkiVo1aoVtmzZgkWLFlWpDWSbFIJQ1dk1qSI5OTnw8vLC3bt3ZVcYcM+ePejdu7dsxs0Q4ypHjKk8Ma7yw5jKE+MqP4ypPMkxrpW9DrXbngBEREREREREVDVMAhARERERERE5CCYBiIiIiIiIiBwEkwBEREREREREDoJJACIiIiIiIiIHwSQAERERERERkYNgEoCIiIiIiIjIQTAJQEREREREROQgmAQgIiIiIiIichBMAhARERERERE5CCYBiIiIiIiIiBwEkwBEREREREREDoJJACIiIiIiIiIH4WztBsiRIAgAgJycHCu3pHrp9XoUFBQgJycHKpXK2s2hasK4yg9jKk+Mq/wwpvLEuMoPYypPcoyr6frTdD1aFiYBLCA3NxcAEBQUZOWWEBERERERkSPJzc2Fl5dXmesVQkVpAqoyo9GI69evw8PDAwqFwtrNqTY5OTkICgrCn3/+CU9PT2s3h6oJ4yo/jKk8Ma7yw5jKE+MqP4ypPMkxroIgIDc3F4GBgVAqyx75z54AFqBUKtGgQQNrN8NiPD09ZXOi0H2Mq/wwpvLEuMoPYypPjKv8MKbyJLe4ltcDwISFAYmIiIiIiIgcBJMARERERERERA6CSQCqNLVajTlz5kCtVlu7KVSNGFf5YUzliXGVH8ZUnhhX+WFM5cmR48rCgEREREREREQOgj0BiIiIiIiIiBwEkwBEREREREREDoJJACIiIiIiIiIHwSQAERERERERkYNgEsDBLVq0CI8//jg8PDxQt25d9O/fH2lpaWbbhIeHQ6FQmD1ee+01s23S09PRp08faDQa1K1bF1OmTIHBYKjJQ6Fi3n333RIxa9GihbS+sLAQY8eORe3ateHu7o4BAwbgxo0bZvtgTG1LcHBwiZgqFAqMHTsWAM9Te3H48GH07dsXgYGBUCgU2Llzp9l6QRAwe/ZsBAQEwNXVFRERETh37pzZNrdv30Z0dDQ8PT3h7e2NkSNHIi8vz2ybX3/9FV26dEGtWrUQFBSEpUuXWvrQHFZ5MdXr9Zg2bRpat24NNzc3BAYGIiYmBtevXzfbR2nn9+LFi822YUxrVkXnamxsbImYRUVFmW3Dc9W2VBTT0v6PVSgUiIuLk7bhuWp7KnMtU13fexMTE9G+fXuo1Wo0bdoU8fHxlj48i2ESwMElJSVh7Nix+O9//4uEhATo9Xr07NkT+fn5ZtuNGjUKGRkZ0qP4H7SioiL06dMHOp0OR48exYYNGxAfH4/Zs2fX9OFQMY8++qhZzI4cOSKtmzBhAr755hts374dSUlJuH79Ol588UVpPWNqe3766SezeCYkJAAABg4cKG3D89T25efno23btli9enWp65cuXYqVK1dizZo1SElJgZubGyIjI1FYWChtEx0djdOnTyMhIQG7d+/G4cOHMXr0aGl9Tk4OevbsiUaNGiE1NRVxcXF499138emnn1r8+BxReTEtKCjA8ePHMWvWLBw/fhxfffUV0tLS8Pzzz5fYdt68eWbn7/jx46V1jGnNq+hcBYCoqCizmH322Wdm63mu2paKYlo8lhkZGVi3bh0UCgUGDBhgth3PVdtSmWuZ6vjee+nSJfTp0wfPPvssTp48ibfeeguvvvoq9u3bV6PHW20EomKysrIEAEJSUpK0rGvXrsKbb75Z5mv27NkjKJVKITMzU1r28ccfC56enoJWq7Vkc6kMc+bMEdq2bVvquuzsbEGlUgnbt2+Xlp05c0YAICQnJwuCwJjagzfffFNo0qSJYDQaBUHgeWqPAAg7duyQnhuNRsHf31+Ii4uTlmVnZwtqtVr47LPPBEEQhN9//10AIPz000/SNt99952gUCiEa9euCYIgCB999JHg4+NjFtdp06YJjzzyiIWPiP4e09IcO3ZMACBcuXJFWtaoUSNhxYoVZb6GMbWu0uI6bNgwoV+/fmW+hueqbavMudqvXz+hW7duZst4rtq+v1/LVNf33qlTpwqPPvqo2Xu9/PLLQmRkpKUPySLYE4DM3L17FwDg6+trtnzLli2oU6cOWrVqhRkzZqCgoEBal5ycjNatW6NevXrSssjISOTk5OD06dM103Aq4dy5cwgMDERISAiio6ORnp4OAEhNTYVer0dERIS0bYsWLdCwYUMkJycDYExtnU6nw+bNmzFixAgoFAppOc9T+3bp0iVkZmaanZteXl4ICwszOze9vb3RsWNHaZuIiAgolUqkpKRI2zzzzDNwcXGRtomMjERaWhru3LlTQ0dDZbl79y4UCgW8vb3Nli9evBi1a9dGu3btEBcXZ9YNlTG1TYmJiahbty4eeeQRjBkzBrdu3ZLW8Vy1bzdu3MC3336LkSNHlljHc9W2/f1aprq+9yYnJ5vtw7SNaR/2xtnaDSDbYTQa8dZbb+Hpp59Gq1atpOVDhgxBo0aNEBgYiF9//RXTpk1DWloavvrqKwBAZmam2UkDQHqemZlZcwdAkrCwMMTHx+ORRx5BRkYG5s6diy5duuDUqVPIzMyEi4tLiS+g9erVk+LFmNq2nTt3Ijs7G7GxsdIynqf2zxSH0uJU/NysW7eu2XpnZ2f4+vqabdO4ceMS+zCt8/HxsUj7qWKFhYWYNm0aBg8eDE9PT2n5G2+8gfbt28PX1xdHjx7FjBkzkJGRgeXLlwNgTG1RVFQUXnzxRTRu3BgXLlzA22+/jV69eiE5ORlOTk48V+3chg0b4OHhYdZlHOC5autKu5apru+9ZW2Tk5ODe/fuwdXV1RKHZDFMApBk7NixOHXqlNnYcQBm49dat26NgIAAdO/eHRcuXECTJk1quplUCb169ZL+3aZNG4SFhaFRo0b4/PPP7e6PFJW0du1a9OrVC4GBgdIynqdEtk2v1+Oll16CIAj4+OOPzdZNnDhR+nebNm3g4uKCf/7zn1i0aBHUanVNN5UqYdCgQdK/W7dujTZt2qBJkyZITExE9+7drdgyqg7r1q1DdHQ0atWqZbac56ptK+tahkricAACAIwbNw67d+/GoUOH0KBBg3K3DQsLAwCcP38eAODv71+iwqbpub+/vwVaS1Xl7e2N5s2b4/z58/D394dOp0N2drbZNjdu3JDixZjaritXruD777/Hq6++Wu52PE/tjykOpcWp+LmZlZVltt5gMOD27ds8f22YKQFw5coVJCQkmPUCKE1YWBgMBgMuX74MgDG1ByEhIahTp47Z31yeq/bphx9+QFpaWoX/zwI8V21JWdcy1fW9t6xtPD097fIGG5MADk4QBIwbNw47duzAwYMHS3RhKs3JkycBAAEBAQCATp064bfffjP7z870Jadly5YWaTdVTV5eHi5cuICAgAB06NABKpUKBw4ckNanpaUhPT0dnTp1AsCY2rL169ejbt266NOnT7nb8Ty1P40bN4a/v7/ZuZmTk4OUlBSzczM7OxupqanSNgcPHoTRaJQSP506dcLhw4eh1+ulbRISEvDII4+wK6oVmBIA586dw/fff4/atWtX+JqTJ09CqVRK3ckZU9t39epV3Lp1y+xvLs9V+7R27Vp06NABbdu2rXBbnqvWV9G1THV97+3UqZPZPkzbmPZhd6xcmJCsbMyYMYKXl5eQmJgoZGRkSI+CggJBEATh/Pnzwrx584Sff/5ZuHTpkvD1118LISEhwjPPPCPtw2AwCK1atRJ69uwpnDx5Uti7d6/g5+cnzJgxw1qH5fAmTZokJCYmCpcuXRJ+/PFHISIiQqhTp46QlZUlCIIgvPbaa0LDhg2FgwcPCj///LPQqVMnoVOnTtLrGVPbVFRUJDRs2FCYNm2a2XKep/YjNzdXOHHihHDixAkBgLB8+XLhxIkTUqX4xYsXC97e3sLXX38t/Prrr0K/fv2Exo0bC/fu3ZP2ERUVJbRr105ISUkRjhw5IjRr1kwYPHiwtD47O1uoV6+eMHToUOHUqVPCtm3bBI1GI3zyySc1fryOoLyY6nQ64fnnnxcaNGggnDx50uz/WVPF6aNHjworVqwQTp48KVy4cEHYvHmz4OfnJ8TExEjvwZjWvPLimpubK0yePFlITk4WLl26JHz//fdC+/bthWbNmgmFhYXSPniu2paK/v4KgiDcvXtX0Gg0wscff1zi9TxXbVNF1zKCUD3fey9evChoNBphypQpwpkzZ4TVq1cLTk5Owt69e2v0eKsLkwAODkCpj/Xr1wuCIAjp6enCM888I/j6+gpqtVpo2rSpMGXKFOHu3btm+7l8+bLQq1cvwdXVVahTp44wadIkQa/XW+GISBDEKUsCAgIEFxcXoX79+sLLL78snD9/Xlp/79494fXXXxd8fHwEjUYjvPDCC0JGRobZPhhT27Nv3z4BgJCWlma2nOep/Th06FCpf3OHDRsmCII4TeCsWbOEevXqCWq1WujevXuJeN+6dUsYPHiw4O7uLnh6egrDhw8XcnNzzbb55ZdfhM6dOwtqtVqoX7++sHjx4po6RIdTXkwvXbpU5v+zhw4dEgRBEFJTU4WwsDDBy8tLqFWrlhAaGiosXLjQ7GJSEBjTmlZeXAsKCoSePXsKfn5+gkqlEho1aiSMGjXKbHoxQeC5amsq+vsrCILwySefCK6urkJ2dnaJ1/NctU0VXcsIQvV97z106JDw2GOPCS4uLkJISIjZe9gbhSAIgoU6GRARERERERGRDWFNACIiIiIiIiIHwSQAERERERERkYNgEoCIiIiIiIjIQTAJQEREREREROQgmAQgIiIiIiIichBMAhARERERERE5CCYBiIiIiIiIiBwEkwBEREREREREDoJJACIiIjITGxuL4OBgazeDiIiILIBJACIiIgegUCgq9UhMTLR2Uyv00UcfIT4+3trNICIisksKQRAEazeCiIiILGvz5s1mzzdu3IiEhARs2rTJbHmPHj3g6+sLo9EItVpdk02stFatWqFOnTp2kbAgIiKyNc7WbgARERFZ3iuvvGL2/L///S8SEhJKLCciIiJ543AAIiIiMvP3mgCXL1+GQqHAsmXLsHr1aoSEhECj0aBnz574888/IQgC5s+fjwYNGsDV1RX9+vXD7du3S+z3u+++Q5cuXeDm5gYPDw/06dMHp0+fNtsmMzMTw4cPR4MGDaBWqxEQEIB+/frh8uXLAIDg4GCcPn0aSUlJ0hCG8PBw6fXZ2dl46623EBQUBLVajaZNm2LJkiUwGo2lHs+KFSvQqFEjuLq6omvXrjh16lSV2kNERGRv2BOAiIiIKmXLli3Q6XQYP348bt++jaVLl+Kll15Ct27dkJiYiGnTpuH8+fNYtWoVJk+ejHXr1kmv3bRpE4YNG4bIyEgsWbIEBQUF+Pjjj9G5c2ecOHFCSjoMGDAAp0+fxvjx4xEcHIysrCwkJCQgPT0dwcHB+OCDDzB+/Hi4u7vjnXfeAQDUq1cPAFBQUICuXbvi2rVr+Oc//4mGDRvi6NGjmDFjBjIyMvDBBx+YHc/GjRuRm5uLsWPHorCwEP/617/QrVs3/Pbbb9I+K2oPERGR3RGIiIjI4YwdO1Yo62vAsGHDhEaNGknPL126JAAQ/Pz8hOzsbGn5jBkzBABC27ZtBb1eLy0fPHiw4OLiIhQWFgqCIAi5ubmCt7e3MGrUKLP3yczMFLy8vKTld+7cEQAIcXFx5bb90UcfFbp27Vpi+fz58wU3Nzfh7NmzZsunT58uODk5Cenp6WbH4+rqKly9elXaLiUlRQAgTJgwoUrtISIisiccDkBERESVMnDgQHh5eUnPw8LCAIj1Bpydnc2W63Q6XLt2DQCQkJCA7OxsDB48GDdv3pQeTk5OCAsLw6FDhwAArq6ucHFxQWJiIu7cuVPl9m3fvh1dunSBj4+P2ftERESgqKgIhw8fNtu+f//+qF+/vvT8iSeeQFhYGPbs2VMt7SEiIrJFHA5AREREldKwYUOz56aEQFBQUKnLTRfO586dAwB069at1P16enoCANRqNZYsWYJJkyahXr16ePLJJ/Hcc88hJiYG/v7+Fbbv3Llz+PXXX+Hn51fq+qysLLPnzZo1K7FN8+bN8fnnn1dLe4iIiGwRkwBERERUKU5OTlVaLvw1C7GpKN+mTZtKvXgu3ovgrbfeQt++fbFz507s27cPs2bNwqJFi3Dw4EG0a9eu3PYZjUb06NEDU6dOLXV98+bNy319aR6mPURERLaISQAiIiKyqCZNmgAA6tati4iIiEptP2nSJEyaNAnnzp3DY489hvfffx+bN28GACgUijJfl5eXV6n3AO73UCju7NmzJQr+VdQeIiIie8KaAERERGRRkZGR8PT0xMKFC6HX60us/9///gdArO5fWFhotq5Jkybw8PCAVquVlrm5uSE7O7vEfl566SUkJydj3759JdZlZ2fDYDCYLdu5c6dUtwAAjh07hpSUFPTq1atK7SEiIrIn7AlAREREFuXp6YmPP/4YQ4cORfv27TFo0CD4+fkhPT0d3377LZ5++ml8+OGHOHv2LLp3746XXnoJLVu2hLOzM3bs2IEbN25g0KBB0v46dOiAjz/+GO+99x6aNm2KunXrolu3bpgyZQp27dqF5557DrGxsejQoQPy8/Px22+/4YsvvsDly5dRp04daT9NmzZF586dMWbMGGi1WnzwwQeoXbu2NJygsu0hIiKyJ0wCEBERkcUNGTIEgYGBWLx4MeLi4qDValG/fn106dIFw4cPByAWGBw8eDAOHDiATZs2wdnZGS1atMDnn3+OAQMGSPuaPXs2rly5gqVLlyI3Nxddu3ZFt27doNFokJSUhIULF2L79u3YuHEjPD090bx5c8ydO9dsZgMAiImJgVKpxAcffICsrCw88cQT+PDDDxEQEFCl9hAREdkThWCq2kNERETkAC5fvozGjRsjLi4OkydPtnZziIiIahRrAhARERERERE5CCYBiIiIiIiIiBwEkwBEREREREREDoI1AYiIiIiIiIgcBHsCEBERERERETkIJgGIiIiIiIiIHASTAEREREREREQOgkkAIiIiIiIiIgfBJAARERERERGRg2ASgIiIiIiIiMhBMAlARERERERE5CCYBCAiIiIiIiJyEP8P+a/tIMshULwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 224 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [0.022678571428571326, 0.16218749999999993, 0.1153124999999999, 0.08281249999999989, 0.0009374999999999106, 0.2359374999999999, 0.1318749999999999, 0.26093749999999993, 0.26781249999999995, 0.18781249999999994, 0.028749999999999915, -0.040937500000000085, -0.0006250000000000699, 0.17031249999999992, 0.271875, 0.20374999999999993, 0.24968749999999995, 0.25156249999999997, 0.3553125, 0.20374999999999993, 0.23187499999999994, 0.20781249999999996, 0.02999999999999997, 0.195625]\n",
+      "Min Rewards: [-1.03, -0.30000000000000027, -0.31000000000000016, -0.3500000000000002, -1.01, -0.2200000000000002, -1.03, -0.28000000000000025, -0.2400000000000002, -0.33000000000000007, -1.03, -1.05, -1.06, -1.04, -0.15000000000000013, -0.2400000000000002, -0.08000000000000007, -0.1100000000000001, -1.03, -0.30000000000000027, -0.45000000000000007, -0.18000000000000005, -0.1200000000000001, -0.08000000000000007]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
+      "Timesteps: [493, 765, 1046, 1336, 1640, 1907, 2172, 2411, 2667, 2944, 3220, 3485, 3761, 4001, 4297, 4586, 4885, 5173, 5415, 5700, 5975, 6280, 6624, 6943]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 493 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [0.11729166666666656, 0.2149999999999999, 0.14062499999999994, -0.0068750000000000755, 0.17593749999999994, 0.1378124999999999, -0.0037500000000001144, 0.09906249999999991, 0.30468749999999994, 0.1556249999999999, 0.14843749999999994, 0.2975, 0.38874999999999993, 0.14406249999999995, 0.2746874999999999, 0.34124999999999994, 0.40499999999999997, 0.25187499999999996, 0.22062499999999996, 0.156875, 0.09562499999999997, 0.1646875, 0.06468749999999998, 0.16093749999999998, 0.04718749999999997, 0.1278125, -0.003437500000000017, 0.0024999999999999745, 0.30749999999999994, 0.09218749999999999, 0.032812499999999974, -0.030000000000000027, 0.0012499999999999734, -0.030000000000000027, 0.0034374999999999753, -0.029687500000000026, -0.030000000000000027, -0.030000000000000027, 0.06687499999999996, 0.10062499999999996, 0.08343749999999998, 0.34843749999999996, 0.28656249999999994, 0.28062499999999996, 0.5615625, 0.0037499999999999756, 0.09499999999999997, 0.0703125, 0.001874999999999974, 0.034687499999999975, -0.00250000000000003, 0.034374999999999975, 0.29093749999999996, -0.028750000000000026, -0.026562500000000024, -2.7755575615628914e-17, 0.0037499999999999756, -0.030000000000000027, 0.034999999999999976, 0.0028124999999999747]\n",
+      "Min Rewards: [-1.05, -1.03, -1.02, -1.02, -0.26000000000000023, -1.03, -0.39000000000000024, -0.31000000000000005, -0.2100000000000002, -0.30000000000000016, -0.17999999999999994, -0.1200000000000001, -0.13000000000000012, -0.10000000000000009, -0.09000000000000008, -0.10000000000000009, -0.1100000000000001, -0.1100000000000001, -0.08000000000000007, -0.08000000000000007, -0.08000000000000007, -0.040000000000000036, -0.06000000000000005, -0.10000000000000009, -0.15000000000000002, -0.07000000000000006, -0.09999999999999987, -0.030000000000000027, -0.15000000000000013, -0.07000000000000006, -0.08000000000000007, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.09000000000000008, -0.1100000000000001, -0.09000000000000008, -0.09000000000000008, -0.1100000000000001, -0.030000000000000027, -0.08000000000000007, -0.040000000000000036, -0.06000000000000005, -0.030000000000000027, -0.08000000000000007, -0.08000000000000007, -0.09000000000000008, -0.030000000000000027, -0.030000000000000027, -0.08000000000000007, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.030000000000000027, 0.97, -0.030000000000000027, 1.0, -0.020000000000000018, -0.030000000000000027, -0.030000000000000027, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 1.0, 1.0, -0.020000000000000018, -0.020000000000000018, 1.0, 1.0, -0.030000000000000027, 1.0, 1.0]\n",
+      "Timesteps: [398, 644, 905, 1208, 1478, 1742, 2052, 2338, 2589, 2875, 3164, 3436, 3689, 4007, 4288, 4580, 4842, 5137, 5448, 5766, 6101, 6413, 6737, 7060, 7398, 7712, 8056, 8400, 8678, 9019, 9359, 9711, 10058, 10410, 10762, 11114, 11466, 11818, 12148, 12461, 12789, 13063, 13332, 13609, 13839, 14191, 14519, 14862, 15210, 15550, 15897, 16237, 16544, 16896, 17248, 17592, 17938, 18290, 18628, 18974]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 398 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [-0.1047916666666668, 0.2165624999999999, -0.11812500000000009, -0.0856250000000001, -0.09625000000000013, 0.1849999999999999, 0.14874999999999994, -0.018437500000000107, 0.042187499999999906, 0.27531249999999996, 0.26874999999999993, 0.17968749999999994, 0.1918749999999999, 0.4534375, 0.4881249999999999, 0.06906249999999994, 0.007812499999999931, 0.25968749999999996, 0.219375, 0.30656249999999996, 0.28312499999999996, 0.25281249999999994, 0.24124999999999994, 0.06218749999999996, 0.15343749999999998, 0.3521875, 0.19249999999999998, 0.0678125, 0.001874999999999974, 0.06843749999999998, 0.033437499999999974, 0.25406249999999997, 0.052812499999999964, -0.029062500000000026, 0.24749999999999994, 0.09781249999999997, 0.17187499999999997, 0.24093749999999997, 0.35093749999999996, 0.20874999999999996, 0.09281249999999996, 0.033437499999999974, 0.09718749999999998, 0.14531249999999996, 0.06718749999999997, 0.034687499999999975, 0.1846875, -0.029687500000000026, 0.32187499999999997, -0.00031250000000002803, 0.06562499999999998, 0.03249999999999997, 0.34624999999999995, 0.06781249999999997, -0.030000000000000027, -0.029062500000000026, 0.060624999999999964, 0.12062499999999998, 0.19343749999999996, 0.16437499999999997, 0.033124999999999974, -0.03156250000000003, 0.13124999999999998, 0.034687499999999975, 0.034374999999999975, 0.06718749999999998, 0.06593749999999998, 0.228125, 0.21812499999999999, 0.13499999999999998, -0.028125000000000025, 0.03687499999999998, -0.028125000000000025, -0.029687500000000026]\n",
+      "Min Rewards: [-1.03, -1.03, -1.02, -1.06, -1.01, -1.01, -0.30000000000000027, -0.41000000000000003, -0.28000000000000014, -0.20000000000000018, -0.22000000000000008, -0.3300000000000002, -0.3600000000000002, -0.2500000000000002, -0.31000000000000016, -1.01, -1.04, -0.16000000000000014, -0.08000000000000007, -0.09000000000000008, -0.1100000000000001, -0.08000000000000007, -0.1100000000000001, -0.08000000000000007, -0.09000000000000008, -0.08000000000000007, -0.08000000000000007, -0.030000000000000027, -0.08000000000000007, -0.040000000000000036, -0.08000000000000007, -0.08000000000000007, -0.09000000000000008, -0.030000000000000027, -0.14000000000000012, -0.06000000000000005, -0.1200000000000001, -0.10000000000000009, -0.08000000000000007, -0.16000000000000014, -0.1100000000000001, -0.030000000000000027, -0.08000000000000007, -0.14000000000000012, -0.030000000000000027, -0.030000000000000027, -0.1100000000000001, -0.030000000000000027, -0.08000000000000007, -0.06000000000000005, -0.030000000000000027, -0.06000000000000005, -0.10000000000000009, -0.030000000000000027, -0.050000000000000044, -0.030000000000000027, -0.08000000000000007, -0.10000000000000009, -0.08000000000000007, -0.030000000000000027, -0.08000000000000007, -0.08000000000000007, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.08000000000000007, -0.040000000000000036, -0.09000000000000008, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, -0.040000000000000036, -0.030000000000000027]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 1.0, 1.0, -0.020000000000000018, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, 0.97, 1.0, 1.0, 1.0, 1.0, -0.020000000000000018, -0.020000000000000018, 0.97, 1.0, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, -0.020000000000000018, -0.020000000000000018]\n",
+      "Timesteps: [438, 672, 979, 1272, 1582, 1840, 2108, 2421, 2729, 2986, 3249, 3524, 3788, 3998, 4220, 4503, 4811, 5096, 5401, 5689, 5984, 6292, 6575, 6913, 7238, 7533, 7836, 8171, 8523, 8857, 9201, 9501, 9844, 10196, 10485, 10828, 11127, 11416, 11699, 11990, 12326, 12663, 12994, 13309, 13647, 13985, 14287, 14639, 14913, 15264, 15606, 15951, 16237, 16579, 16931, 17283, 17612, 17927, 18248, 18568, 18908, 19260, 19588, 19934, 20284, 20623, 20975, 21274, 21579, 21923, 22275, 22617, 22969, 23321]\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 438 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [0.03749999999999991, 0.038749999999999896, -0.18875000000000014, -0.09937500000000007, -0.1181250000000001, -0.0718750000000001, -0.0643750000000001, -0.2556250000000001, -0.15500000000000008, -0.27000000000000013, -0.17500000000000004, -0.2250000000000001, -0.1543750000000001, -0.19812500000000016, 0.12812499999999996, 0.06437499999999995, 0.09937499999999998, 0.09749999999999998, -0.030000000000000027, 0.034999999999999976, 0.027499999999999976, 0.09187499999999997, 0.08437499999999996, 0.06812499999999996, 0.08562499999999997, 0.5237499999999999, 0.15499999999999997, 0.33625, 0.0399999999999999, 0.08874999999999997, -0.030000000000000027, 0.29, -0.030000000000000027, 0.09999999999999998, -0.030000000000000027, 0.026249999999999968, -0.029375000000000026, 0.23999999999999994, 0.6725, 0.39374999999999993, 0.33812499999999995, 0.06562499999999995, 0.33562499999999995, 0.47312499999999996, 0.26187499999999997, 0.21437499999999998, 0.27749999999999997, 0.33062499999999995, 0.5399999999999999, 0.33499999999999996, 0.38999999999999996, 0.535625, 0.2537499999999999, 0.3475, 0.27249999999999996, 0.22374999999999998, 0.22499999999999998, 0.5475, 0.33749999999999997, 0.13874999999999998, 0.33624999999999994, 0.48, 0.02937499999999997, -0.029375000000000026, 0.01937499999999996, -0.030000000000000027, -0.029375000000000026, 0.161875, 0.14249999999999996, 0.411875, -0.030000000000000027, -0.02437500000000002, -0.030000000000000027, -0.030000000000000027, 0.09937499999999998, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, 0.284375, 0.15812499999999996, 0.02999999999999997, 0.06562499999999996, 0.09374999999999997, 0.16312499999999996, 0.034374999999999975, 0.2875, 0.35124999999999995, -0.030000000000000027, -0.03562500000000004, -0.010625000000000058, 0.09687499999999998, 0.029374999999999943, 0.034999999999999976, -0.030000000000000027, 0.15124999999999994, 0.41375, 0.41625, 0.159375, 0.08499999999999996, 0.20749999999999996, -0.04125000000000004, 0.22749999999999998, -0.029375000000000026, -0.03187500000000003, 0.08999999999999997, 0.14437499999999998, 0.008124999999999959, 0.466875, 0.25875, 0.32562499999999994, 0.336875, 0.05124999999999996, 0.47062499999999996, 0.18624999999999997, 0.23749999999999988, 0.78, -0.030000000000000027, 0.28312499999999996, 0.31562499999999993, 0.323125, -0.1050000000000001, 0.34562499999999996, 0.14374999999999996, 0.7037499999999999, 0.23999999999999994, 0.051874999999999935, 0.03749999999999992, 0.14687499999999998, 0.5081249999999999, 0.18374999999999997, 0.21999999999999997, -0.051250000000000046, 0.25687499999999996, 0.26999999999999996, -0.030000000000000027, 0.5262499999999999, 0.03062499999999997, -0.04375000000000004, -0.051875000000000046, 0.38125, 0.14312499999999995, -0.03687500000000003, -0.07500000000000007, -0.01687500000000007, 0.21562499999999996, 0.06249999999999997, 0.27749999999999997, 0.711875, 0.25999999999999995, 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+      "Max Rewards: [1.0, 1.0, -0.08000000000000007, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 0.0, -0.020000000000000018, 1.0, -0.030000000000000027, -0.030000000000000027, -0.020000000000000018, 1.0, 1.0, 1.0, 1.0, -0.030000000000000027, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 1.0, 1.0, 1.0, -0.030000000000000027, 1.0, -0.030000000000000027, 1.0, -0.030000000000000027, 0.97, -0.020000000000000018, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 1.0, 1.0, 0.97, -0.020000000000000018, 0.97, -0.030000000000000027, -0.020000000000000018, 1.0, 1.0, 1.0, -0.030000000000000027, -0.020000000000000018, -0.030000000000000027, -0.030000000000000027, 1.0, -0.030000000000000027, -0.030000000000000027, -0.030000000000000027, 1.0, 0.97, 0.97, 1.0, 0.97, 1.0, 1.0, 1.0, 1.0, -0.030000000000000027, 1.0, 0.97, 1.0, 1.0, 1.0, -0.030000000000000027, 1.0, 1.0, 1.0, 1.0, 0.97, 1.0, -0.020000000000000018, 1.0, -0.020000000000000018, -0.020000000000000018, 1.0, 0.97, 0.97, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 1.0, 0.97, 1.0, -0.030000000000000027, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, 1.0, 1.0, 0.97, 0.97, 1.0, 1.0, 1.0, 1.0, 1.0, -0.030000000000000027, 0.97, 0.97, -0.030000000000000027, 1.0, 1.0, -0.020000000000000018, -0.030000000000000027, 0.97, 1.0, -0.020000000000000018, -0.050000000000000044, 0.97, 0.97, 1.0, 1.0, 1.0, 0.97, 1.0, 0.97, 0.97, 1.0, 0.97, -0.030000000000000027, -0.020000000000000018, 1.0, 1.0, 1.0, 0.97, 1.0, 0.97, 0.97, -0.040000000000000036, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, 0.97, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, 1.0, 0.98, 1.0, 0.97, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 0.97, 1.0, 1.0, 1.0, -0.020000000000000018, 0.97, 0.97, 1.0, 0.97, 0.97, 0.97, 0.97, 1.0, -0.040000000000000036, 1.0, 0.97, 1.0, 1.0, 1.0, 0.97, 0.97, 1.0, 1.0, 1.0, 0.97, 0.97, 1.0, 0.97, 1.0, 1.0, 0.97, 1.0, 1.0, 1.0, 0.97, 0.97, 1.0, 1.0, 1.0, 0.97, 0.97, 0.97, 0.97, -0.020000000000000018, -0.06000000000000005, 1.0, 1.0, 0.97, 1.0, 1.0, -0.010000000000000009, 0.97, 0.97, 1.0, 1.0, 0.97, 0.97, 0.97, 1.0, 0.97, 0.97, 1.0, 0.97, 0.97, -0.030000000000000027, -0.010000000000000009, -0.010000000000000009, 0.97, 0.97, 0.97, 0.97, -0.040000000000000036, 1.0, 0.97, 0.97, 0.97, 0.97, -0.020000000000000018, 0.97, 0.97, -0.010000000000000009, 0.97, 1.0, 0.97, 1.0, 0.97, 1.0, -0.020000000000000018, 0.97, -0.040000000000000036, 1.0, 0.97, 1.0, 0.97, 0.97, -0.020000000000000018, 1.0, -0.020000000000000018, 0.97, -0.010000000000000009, 1.0, 1.0, 1.0, 0.97, -0.010000000000000009, 0.97, 0.97, 0.97, 0.97, 1.0, 0.97, 0.97, 1.0, 0.97, -0.010000000000000009, 0.97, -0.010000000000000009, 0.97, 0.97, -0.020000000000000018, -0.020000000000000018, 1.0, 0.97, 0.97, 1.0, -0.020000000000000018, -0.010000000000000009, 1.0, 1.0, -0.010000000000000009, -0.020000000000000018, -0.020000000000000018, -0.020000000000000018, -0.020000000000000018, -0.020000000000000018, -0.020000000000000018, -0.020000000000000018, -0.020000000000000018, -0.010000000000000009, -0.020000000000000018, 0.98, -0.010000000000000009, 1.0, -0.020000000000000018, -0.020000000000000018, 1.0, 1.0, -0.020000000000000018, -0.020000000000000018, -0.010000000000000009, -0.020000000000000018, -0.020000000000000018, 1.0, 1.0, 0.98, 1.0, 1.0, 1.0, 0.97, 0.97, -0.010000000000000009, 0.97, 0.97, 0.98, 1.0, -0.030000000000000027, 1.0, 0.98, 1.0, -0.020000000000000018, -0.010000000000000009, 1.0, 1.0, 1.0, 1.0, 1.0, -0.010000000000000009, 1.0, -0.020000000000000018, 0.97, -0.010000000000000009, 0.98, 0.97, 1.0, -0.020000000000000018, -0.020000000000000018, 0.97, -0.020000000000000018, -0.020000000000000018, -0.020000000000000018, 0.97, 0.97, -0.030000000000000027, 1.0, 1.0, -0.030000000000000027, 0.98, 1.0, -0.040000000000000036, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, 0.97, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.020000000000000018, 1.0, 1.0, 1.0, -0.020000000000000018, -0.020000000000000018, -0.030000000000000027, 1.0]\n",
+      "Timesteps: [291, 420, 596, 751, 919, 1078, 1230, 1402, 1578, 1742, 1882, 2050, 2226, 2402, 2553, 2715, 2879, 3044, 3220, 3388, 3564, 3727, 3886, 4049, 4208, 4298, 4453, 4582, 4735, 4892, 5068, 5213, 5389, 5557, 5733, 5902, 6078, 6206, 6306, 6421, 6546, 6708, 6826, 6924, 7053, 7215, 7341, 7468, 7565, 7687, 7813, 7927, 8056, 8200, 8331, 8469, 8612, 8719, 8846, 9003, 9136, 9240, 9409, 9585, 9754, 9930, 10106, 10266, 10424, 10561, 10737, 10913, 11089, 11265, 11429, 11605, 11781, 11957, 12107, 12276, 12445, 12606, 12768, 12930, 13100, 13235, 13377, 13553, 13719, 13888, 14054, 14217, 14385, 14561, 14724, 14862, 14998, 15156, 15324, 15478, 15654, 15818, 15994, 16170, 16330, 16485, 16652, 16765, 16896, 17035, 17178, 17346, 17462, 17615, 17754, 17823, 17999, 18127, 18250, 18382, 18558, 18678, 18829, 18905, 19044, 19216, 19375, 19531, 19631, 19779, 19929, 20105, 20238, 20383, 20559, 20674, 20848, 21024, 21200, 21337, 21501, 21677, 21853, 22022, 22168, 22331, 22468, 22547, 22684, 22801, 22928, 23085, 23177, 23344, 23520, 23696, 23809, 23952, 24096, 24240, 24277, 24381, 24530, 24706, 24863, 24979, 25127, 25257, 25433, 25600, 25749, 25895, 26055, 26213, 26389, 26545, 26713, 26830, 26980, 27139, 27273, 27429, 27597, 27729, 27876, 28001, 28157, 28306, 28454, 28567, 28709, 28833, 28982, 29158, 29329, 29480, 29641, 29803, 29873, 30010, 30144, 30272, 30448, 30596, 30723, 30863, 30958, 31064, 31206, 31373, 31535, 31680, 31828, 31990, 32139, 32283, 32450, 32597, 32730, 32890, 33046, 33179, 33317, 33413, 33559, 33689, 33832, 33993, 34148, 34306, 34477, 34627, 34803, 34979, 35125, 35295, 35419, 35575, 35732, 35908, 36045, 36174, 36300, 36463, 36621, 36744, 36873, 37031, 37176, 37334, 37501, 37672, 37815, 37991, 38167, 38343, 38510, 38679, 38837, 38999, 39175, 39316, 39449, 39615, 39782, 39931, 40107, 40280, 40447, 40623, 40729, 40883, 41052, 41218, 41320, 41489, 41665, 41829, 42005, 42119, 42258, 42423, 42565, 42720, 42896, 43042, 43218, 43393, 43569, 43738, 43889, 44058, 44227, 44403, 44576, 44736, 44878, 45027, 45186, 45337, 45490, 45649, 45816, 45992, 46163, 46339, 46512, 46681, 46857, 47033, 47198, 47369, 47542, 47709, 47885, 48061, 48216, 48383, 48559, 48735, 48911, 49087, 49263, 49439, 49615, 49791, 49967, 50143, 50319, 50485, 50661, 50828, 51004, 51180, 51339, 51505, 51681, 51857, 52033, 52209, 52385, 52551, 52721, 52890, 53046, 53193, 53348, 53517, 53687, 53863, 54013, 54177, 54348, 54514, 54690, 54859, 55028, 55201, 55377, 55553, 55712, 55879, 56036, 56202, 56361, 56537, 56711, 56887, 57056, 57232, 57401, 57576, 57734, 57910, 58086, 58261, 58437, 58613, 58789, 58962, 59137, 59313, 59446, 59614, 59790, 59957, 60115, 60291, 60457, 60603, 60759, 60935, 61090, 61261, 61427, 61573, 61740, 61907, 62065, 62211, 62357, 62497, 62653, 62802, 62978, 63121, 63267, 63421, 63597, 63773, 63942, 64102]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 291 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [0.3162499999999999, 0.24374999999999994, 0.039999999999999925, 0.49624999999999997]\n",
+      "Min Rewards: [-0.26000000000000023, -0.28000000000000025, -0.2400000000000002, -0.2300000000000002]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0]\n",
+      "Timesteps: [290, 416, 572, 666]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 290 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [0.24152777777777773, 0.28812499999999996, 0.15874999999999997, 0.16062499999999996, 0.031249999999999972, 0.66875, 0.54125, 0.27374999999999994, 0.16062499999999996, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, 0.28562499999999996, 0.08687499999999997, 0.02687499999999997, -0.040000000000000036]\n",
+      "Min Rewards: [-0.1100000000000001, -0.08000000000000007, -0.07000000000000006, -0.09000000000000008, -0.08000000000000007, -0.1100000000000001, -0.1100000000000001, -0.09000000000000008, -0.1100000000000001, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, -0.07000000000000006, -0.08000000000000007, -0.08000000000000007, -0.040000000000000036]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -0.040000000000000036, -0.040000000000000036, -0.040000000000000036, 1.0, 1.0, 1.0, -0.040000000000000036]\n",
+      "Timesteps: [176, 220, 264, 306, 352, 380, 410, 452, 494, 542, 590, 638, 676, 720, 766, 814]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 176 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [0.24208333333333332, 0.3475, 0.34312499999999996, 0.21624999999999997, 0.41687499999999994, 0.41374999999999995, 0.93375, 0.808125, 1.0, 1.0, 1.0]\n",
+      "Min Rewards: [-0.1100000000000001, -0.08000000000000007, -0.10000000000000009, -0.1100000000000001, -0.09000000000000008, -0.10000000000000009, -0.06000000000000005, -0.040000000000000036, 1.0, 1.0, 1.0]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
+      "Timesteps: [176, 220, 258, 298, 334, 372, 390, 412, 428, 448, 464]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 176 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "abel-rl",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zoo/custom_envs/equation_solver/read_learning_curve_muzero.ipynb b/zoo/custom_envs/equation_solver/read_learning_curve_muzero.ipynb
new file mode 100644
index 000000000..04550b001
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/read_learning_curve_muzero.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "### Notes\n",
+    "\n",
+    "1. Might want to increase the MAX_STEP and equation length -- muzero might be able to exploit longer things. \n",
+    "2. Check how large the depth search is\n",
+    "3. Action masking, illegal action.\n",
+    "4. Debug the env... there is some shenanigans going on... aren't illegal equations supposedly impossible?\n",
+    "5. Then curriculum learning\n",
+    "\n",
+    "\n",
+    "### x+b = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [-0.026250000000000124, 0.1499999999999999, -0.11000000000000008, 0.4262499999999999, 0.5762499999999999, -0.12625000000000008, 0.02874999999999993, 0.2849999999999999, 0.44375, 0.57375, 0.3999999999999999, 0.4237499999999999, -0.013750000000000082, 0.44875, 0.46624999999999994, 0.45749999999999996, -0.10000000000000009, 0.3112499999999999, 0.012499999999999928, 0.18999999999999995, 0.44499999999999995, 0.19624999999999995, 0.86625, 0.43999999999999995, 0.29999999999999993, 0.59, -0.06625000000000004, 0.46249999999999997, 0.5599999999999999]\n",
+      "Min Rewards: [-0.39000000000000024, -0.19000000000000017, -1.0, -0.2200000000000002, -0.22000000000000008, -0.2200000000000002, -0.2600000000000001, -0.18000000000000016, -0.14000000000000012, -0.22000000000000008, -0.30000000000000016, -0.2200000000000002, -0.2400000000000002, -0.10000000000000009, -0.09000000000000008, -0.09000000000000008, -0.20000000000000018, -0.10000000000000009, -0.20000000000000007, -0.09000000000000008, -0.10000000000000009, -0.07000000000000006, -0.040000000000000036, -0.13000000000000012, -0.17000000000000015, -0.09000000000000008, -0.16000000000000003, -0.09000000000000008, -0.20000000000000007]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, -0.06000000000000005, 0.96, 1.0, 1.0, 1.0, 1.0, 1.0, 0.97, 0.97, 1.0, 1.0, -0.06000000000000005, 0.97, 0.97, 0.97, 0.97, 0.97, 1.0, 1.0, 0.97, 1.0, -0.050000000000000044, 1.0, 1.0]\n",
+      "Timesteps: [224, 292, 363, 416, 457, 545, 628, 693, 745, 786, 834, 890, 969, 1033, 1092, 1158, 1246, 1311, 1395, 1469, 1529, 1601, 1651, 1704, 1771, 1817, 1905, 1970, 2022]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 224 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: [0.022678571428571326, 0.16218749999999993, 0.1153124999999999, 0.08281249999999989, 0.0009374999999999106, 0.2359374999999999, 0.1318749999999999, 0.26093749999999993, 0.26781249999999995, 0.18781249999999994, 0.028749999999999915, -0.040937500000000085, -0.0006250000000000699, 0.17031249999999992, 0.271875, 0.20374999999999993, 0.24968749999999995, 0.25156249999999997, 0.3553125, 0.20374999999999993, 0.23187499999999994, 0.20781249999999996, 0.02999999999999997, 0.195625]\n",
+      "Min Rewards: [-1.03, -0.30000000000000027, -0.31000000000000016, -0.3500000000000002, -1.01, -0.2200000000000002, -1.03, -0.28000000000000025, -0.2400000000000002, -0.33000000000000007, -1.03, -1.05, -1.06, -1.04, -0.15000000000000013, -0.2400000000000002, -0.08000000000000007, -0.1100000000000001, -1.03, -0.30000000000000027, -0.45000000000000007, -0.18000000000000005, -0.1200000000000001, -0.08000000000000007]\n",
+      "Max Rewards: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
+      "Timesteps: [493, 765, 1046, 1336, 1640, 1907, 2172, 2411, 2667, 2944, 3220, 3485, 3761, 4001, 4297, 4586, 4885, 5173, 5415, 5700, 5975, 6280, 6624, 6943]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "First max reward > 100 at timestep: 493 with reward: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Rewards: []\n",
+      "Min Rewards: []\n",
+      "Max Rewards: []\n",
+      "Timesteps: []\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The lists are empty. Please check your data.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Path to the experiment logs\n",
+    "data_dir = \"/Users/Kev/Documents/research/LightZero/data_muzero/x+b\"\n",
+    "collector_log_dir = os.path.join(data_dir, \"log\", \"collector\")\n",
+    "\n",
+    "# Initialize storage for reward statistics and episode counts\n",
+    "mean_rewards = []\n",
+    "min_rewards = []\n",
+    "max_rewards = []\n",
+    "timesteps = []  # Will store the total_envstep_count\n",
+    "\n",
+    "# Read the `collector_logger.txt` file\n",
+    "collector_log_file = os.path.join(collector_log_dir, \"collector_logger.txt\")\n",
+    "if os.path.isfile(collector_log_file):\n",
+    "    with open(collector_log_file, \"r\") as f:\n",
+    "        for line in f:\n",
+    "            if \"reward_mean\" in line:\n",
+    "                mean_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_min\" in line:\n",
+    "                min_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"reward_max\" in line:\n",
+    "                max_rewards.append(float(line.split(\":\")[-1].strip()))\n",
+    "            elif \"total_envstep_count\" in line:  # Extract total_envstep_count\n",
+    "                timesteps.append(int(line.split(\":\")[-1].strip()))\n",
+    "else:\n",
+    "    print(f\"No collector log file found at {collector_log_file}\")\n",
+    "    exit()\n",
+    "\n",
+    "# Debug the parsed data\n",
+    "print(\"Mean Rewards:\", mean_rewards)\n",
+    "print(\"Min Rewards:\", min_rewards)\n",
+    "print(\"Max Rewards:\", max_rewards)\n",
+    "print(\"Timesteps:\", timesteps)\n",
+    "\n",
+    "# Ensure reward_errors have non-negative values\n",
+    "reward_errors = [\n",
+    "    [max(0, mean - min_val) for mean, min_val in zip(mean_rewards, min_rewards)],  # Lower error\n",
+    "    [max(0, max_val - mean) for max_val, mean in zip(max_rewards, mean_rewards)]   # Upper error\n",
+    "]\n",
+    "\n",
+    "# Plot the learning curve with error bars\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.errorbar(\n",
+    "    timesteps, mean_rewards,  fmt=\"-o\", capsize=5, label=\"Reward Mean\", color=\"blue\"\n",
+    ")\n",
+    "plt.fill_between(\n",
+    "    timesteps, min_rewards, max_rewards, color=\"blue\", alpha=0.2, label=\"Reward Range (Min-Max)\"\n",
+    ")\n",
+    "plt.xlabel(\"Timesteps\")  # Change from Episodes to Timesteps\n",
+    "plt.ylabel(\"Reward\")\n",
+    "plt.title(\"Solve x+b\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Ensure the lists are not empty\n",
+    "if max_rewards and timesteps:\n",
+    "    for i, reward in enumerate(max_rewards):\n",
+    "        if reward >= 1:\n",
+    "            print(f\"First max reward > 100 at timestep: {timesteps[i]} with reward: {reward}\")\n",
+    "            break\n",
+    "else:\n",
+    "    print(\"The lists are empty. Please check your data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "abel-rl",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zoo/custom_envs/equation_solver/utils_custom_functions.py b/zoo/custom_envs/equation_solver/utils_custom_functions.py
new file mode 100644
index 000000000..5cffb1651
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/utils_custom_functions.py
@@ -0,0 +1,90 @@
+from sympy import simplify, factor, sqrt, Pow, Basic, Integer, Mul, Add, collect, together
+from sympy import Eq, ratsimp, expand, Abs, Rational, Number, sympify, symbols, integrate
+from sympy import sin, cos, tan, asin, acos, atan, Symbol
+from operator import add, sub, mul, truediv
+
+def custom_identity(expr, term):
+    return expr
+
+def custom_expand(expr, term):
+    return expand(expr)
+
+def custom_simplify(expr, term):
+    return simplify(expr)
+
+def custom_factor(expr, term):
+    return factor(expr)
+
+def custom_collect(expr, term):
+    return collect(expr, term)
+
+def custom_together(expr, term):
+    return together(expr)
+
+def custom_ratsimp(expr, term):
+    return ratsimp(expr)
+
+def custom_square(expr, term):
+    return expr**2
+
+def custom_sqrt(expr, term):
+    # Check if the expression is a perfect square
+    simplified_expr = simplify(expr)
+
+    # Case 1: If it's a square of a single term (like x**2), return the term
+    if simplified_expr.is_Pow and simplified_expr.exp == 2:
+        base = simplified_expr.base
+        return base
+
+    # Case 2: Otherwise, return ±sqrt(expression)
+    return sqrt(expr)
+
+def inverse_sin(expr, term):
+    if isinstance(expr, (int, float)):
+        return asin(expr)
+    if expr.has(sin):
+        return expr.replace(
+            lambda arg: arg.func == sin,
+            lambda arg: arg.args[0]
+        )
+    return asin(expr)
+
+def inverse_cos(expr, term):
+    if isinstance(expr, (int, float)):
+        return acos(expr)
+    if expr.has(cos):
+        return expr.replace(
+            lambda arg: arg.func == cos,
+            lambda arg: arg.args[0]
+        )
+    return acos(expr)
+
+def inverse_tan(expr, term):
+    if isinstance(expr, (int, float)):
+        return atan(expr)
+    if expr.has(tan):
+        return expr.replace(
+            lambda arg: arg.func == tan,
+            lambda arg: arg.args[0]
+        )
+    return atan(expr)
+
+
+operation_names = {
+    add: "add",
+    sub: "subtract",
+    mul: "multiply",
+    truediv: "divide",
+    custom_expand: "expand",
+    custom_simplify: "simplify",
+    custom_factor: "factor",
+    custom_collect: "collect",
+    custom_together: "together",
+    custom_ratsimp: "ratsimp",
+    custom_square: "square",
+    custom_sqrt: "sqrt",
+    inverse_sin: 'sin^{-1}',
+    inverse_cos: 'cos^{-1}',
+    inverse_tan: 'tan^{-1}',
+    custom_identity: 'identity'
+}
diff --git a/zoo/custom_envs/equation_solver/utils_env.py b/zoo/custom_envs/equation_solver/utils_env.py
new file mode 100644
index 000000000..5ac44f281
--- /dev/null
+++ b/zoo/custom_envs/equation_solver/utils_env.py
@@ -0,0 +1,839 @@
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import torch
+import numpy as np
+
+from itertools import product
+from functools import lru_cache
+from operator import add, sub, mul, truediv
+from utils_custom_functions import custom_identity
+from sympy import symbols, sympify, simplify, powdenest, ratsimp, E, I, pi, zoo,  Basic, Number, Integer, Float
+from collections import deque
+from torch_geometric.data import Data
+
+############################################### 1D integer encoding ###############################################
+
+
+
+def make_feature_dict(main_eqn, state_rep):
+
+    if state_rep == 'integer_1d' or state_rep == 'graph_integer_1d':
+        return make_feature_dict_integer_1d(main_eqn)
+    elif state_rep == 'integer_2d' or state_rep == 'graph_integer_2d':
+        return make_feature_dict_integer_2d(main_eqn)
+    else:
+        raise ValueError(f"Unsupported encoding type: {encoding}")
+
+
+def make_feature_dict_integer_1d(main_eqn):
+    """
+    Generate a feature dictionary mapping symbols, numbers, and operations to unique integers.
+
+    Args:
+        lhs (sympy.Expr): Left-hand side expression.
+        rhs (sympy.Expr): Right-hand side expression.
+
+    Returns:
+        dict: A dictionary mapping elements to integer encodings.
+
+    Example:
+        >>> from sympy import symbols
+        >>> a, b, x = symbols('a b x')
+        >>> lhs, rhs = a/x + b, 0
+        >>> make_feature_dict(lhs, rhs)
+        {'=': 0, x: 1, a: 2, b: 3, 0: 4, -1: 5, 'add': -1, 'pow': -2, 'mul': -3, 'sqrt': -4}
+    """
+
+    # Build feature dictionary
+    free_symbols = list(main_eqn.free_symbols)
+    special_constants = ['-1', 'I']
+    integers = list(range(0,4))
+    all_symbols = free_symbols + special_constants + integers
+
+    x = symbols('x')
+    feature_dict = {'=': 0, x: 1}
+    ctr = 2
+    for symbol in all_symbols:
+        if symbol not in feature_dict:
+            feature_dict[symbol] = 1.0*ctr
+            ctr += 1
+
+    # Encode operations as negative numbers
+    ctr = -1
+    operations = ['add', 'pow', 'mul', 'sqrt']
+    for operation in operations:
+        feature_dict[operation] = 1.0*ctr
+        ctr -= 1
+
+    return feature_dict
+
+
+def integer_encoding_1d(lhs, rhs, feature_dict, max_length):
+    """
+    Convert symbolic expressions into integer-encoded vectors with padding.
+
+    Args:
+        lhs (sympy.Expr): Left-hand side expression.
+        rhs (sympy.Expr): Right-hand side expression.
+        feature_dict (dict): Dictionary mapping symbols and operations to integers.
+        max_length (int): Maximum allowed length for each expression.
+
+    Returns:
+        np.ndarray: A fixed-length integer vector representing the encoded equation.
+
+    Example:
+        >>> from sympy import symbols
+        >>> a, x = symbols('a x')
+        >>> lhs, rhs = a*x + 2, 0
+        >>> feature_dict = {'=': 0, a: 1, x: 2, 'add': -1, 'mul': -2, 2: 3}
+        >>> integer_encoding(lhs, rhs, feature_dict, max_length=10)
+        array([ 1, -2,  2, -1,  3,  0,  0,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7])
+    """
+
+    L = max_length
+    PAD_ID = len(feature_dict) + 1  # Padding token
+
+    vector = np.full(2 * L + 1, PAD_ID, dtype=np.float32)  # Preallocate with padding
+
+    lhs_as_list, complexity_lhs = sympy_expression_to_list(lhs, feature_dict, L)
+    rhs_as_list, complexity_rhs = sympy_expression_to_list(rhs, feature_dict, L)
+    complexity = complexity_lhs + complexity_rhs
+
+    # Fill array while ensuring the separator `=` is in the middle
+    vector[:len(lhs_as_list)] = lhs_as_list
+    vector[len(lhs_as_list)] = feature_dict["="]  # Middle separator
+    vector[len(lhs_as_list) + 1 : len(lhs_as_list) + 1 + len(rhs_as_list)] = rhs_as_list
+
+    return vector, complexity
+
+
+def sympy_expression_to_list(expr, feature_dict, max_length):
+    """
+    Convert a SymPy expression into a fixed-length integer list using a feature dictionary.
+
+    Args:
+        expr (sympy.Expr): The symbolic expression to encode.
+        feature_dict (dict): Dictionary mapping symbols and operations to integers.
+        max_length (int): Maximum length of the output list.
+
+    Returns:
+        np.ndarray: A fixed-length list representing the expression, padded if necessary.
+
+    Example:
+        #>>> from sympy import symbols
+        #>>> a, x = symbols('a x')
+        #>>> expr = a*x + 2
+        #>>> feature_dict = {'=': 0, a: 1, x: 2, 'add': -1, 'mul': -2, 2: 3}
+        #>>> sympy_expression_to_list(expr, feature_dict, max_length=6)
+        array([ 1, -2,  2, -1,  3,  7])  # 7 is the padding ID
+    """
+
+    PAD_ID = len(feature_dict) + 1  # Padding token
+    elements = np.full(max_length, PAD_ID, dtype=np.int32)  # Preallocate with padding
+    index = 0
+
+    stack = [expr]  # Manual stack to avoid recursion overhead
+    while stack and index < max_length:
+        node = stack.pop()
+
+        if isinstance(node, (int, float)):  # ✅ Handle numbers explicitly
+            elements[index] = feature_dict.get(node, len(feature_dict))  # Assign new ID if missing
+            feature_dict[node] = elements[index]  # Store it to avoid reassignments
+            index += 1
+        elif node.is_Symbol:
+            elements[index] = feature_dict.get(node, 0)  # Default to 0 if missing
+            index += 1
+        elif node.is_Atom:
+            continue  # Skip special cases like `pi` or `E` if needed
+        else:
+            elements[index] = feature_dict.get(node.func.__name__.lower(), 0)
+            index += 1
+            stack.extend(reversed(node.args))  # Push children to stack
+
+    # Complexity measure: num of nodes
+    complexity = index
+
+    return elements, complexity  # Returns fixed-length padded array
+
+
+def get_complexity_expression(expr):
+    """
+    Compute the complexity of a SymPy expression efficiently by counting the number of unique nodes 
+    in its expression tree using an optimized traversal.
+
+    Args:
+        expr (sympy.Expr): The symbolic expression.
+
+    Returns:
+        int: Complexity score (total number of unique nodes in the expression tree).
+
+    Example:
+        >>> from sympy import symbols
+        >>> a, x = symbols('a x')
+        >>> expr = (a*x + 2) / (x + 1)
+        >>> get_complexity_expression(expr)
+        7
+    """
+
+    if expr == 0:
+        return 1  # Complexity of constant zero is 1
+
+    visited = set()
+    stack = deque([expr])  # Use deque for fast pop/push operations
+    complexity = 0
+
+    while stack:
+        node = stack.pop()
+
+        if node in visited:
+            continue  # Avoid redundant processing
+        visited.add(node)
+
+        complexity += 1  # Count this node
+
+        # Add sub-expressions (children) efficiently
+        if node.is_Atom:
+            continue  # Skip atomic symbols and numbers
+        stack.extend(node.args)  # Push children
+
+    return complexity
+
+
+
+def make_actions(lhs, rhs, actions_fixed, action_dim):
+    """Generates a list of possible actions and a valid action mask."""
+    
+    # Dynamic actions (operation, term pairs)
+    operations = [add, sub, mul, truediv]
+    terms = get_ordered_sub_expressions(lhs)  # Extract meaningful terms
+    actions_dynamic = list(product(operations, terms))
+
+    # Combine fixed + dynamic actions
+    actions = actions_fixed + actions_dynamic
+    num_valid_actions = min(len(actions), action_dim)
+
+    # Pre-allocate full-size mask (False by default)
+    valid_action_mask = np.zeros(action_dim, dtype=bool)
+    valid_action_mask[:num_valid_actions] = True  # Mark valid actions
+
+    # Trim or pad with identity ops if needed
+    actions = (actions[:action_dim] + [(custom_identity, None)] * action_dim)[:action_dim]
+
+    return actions, valid_action_mask
+
+
+@lru_cache(maxsize=1000)
+def get_cached_terms(lhs):
+    """Caches the sub-expressions extracted from lhs."""
+    return get_ordered_sub_expressions(lhs)
+
+
+def make_actions_cache(lhs, rhs, actions_fixed, action_dim, cache):
+    """Generates a list of possible actions and a valid action mask with caching."""
+    
+    # Check cache first
+    key = (lhs, rhs)
+    if key in cache:
+        return cache[key]
+
+    # Dynamic actions (operation, term pairs)
+    operations = [add, sub, mul, truediv]
+    terms = get_cached_terms(lhs)  # Use cached sub-expressions
+    actions_dynamic = list(product(operations, terms))
+
+    # Combine fixed + dynamic actions
+    actions = actions_fixed + actions_dynamic
+    num_valid_actions = min(len(actions), action_dim)
+
+    # Pre-allocate full-size mask (False by default)
+    valid_action_mask = np.zeros(action_dim, dtype=bool)
+    valid_action_mask[:num_valid_actions] = True  # Mark valid actions
+
+    # Trim or pad with identity ops if needed
+    actions = (actions[:action_dim] + [(custom_identity, None)] * action_dim)[:action_dim]
+
+    # Store in cache
+    cache[key] = (actions, valid_action_mask)
+
+    return actions, valid_action_mask
+
+
+def get_ordered_sub_expressions(expr):
+    """Extracts all unique sub-expressions from a given symbolic expression, 
+    excluding the full expression itself, and returns them in a fixed order.
+    """
+    
+    if expr == 0:
+        return []
+    
+    sub_expressions = set()
+    queue = [expr]  # Use a queue for BFS traversal to ensure order consistency
+    
+    while queue:
+        e = queue.pop(0)  # BFS: Process oldest element
+        
+        if e not in sub_expressions and 1/e not in sub_expressions and e not in [-1,0,1]:
+        #if e not in sub_expressions and 1/e not in sub_expressions and not e.is_Integer and e not in [-1, 0, 1]:
+
+            sub_expressions.add(e)
+
+            # Only expand non-atomic expressions
+            if not e.is_Atom:
+                queue.extend(e.args)  
+
+    # Exclude full expression and sort
+    sub_expressions.discard(expr)  
+    
+    # Sorting rule:
+    # - Prioritize smallest-length expressions first
+    # - Tie-break by lexicographic order of string representations (stable mapping)
+    #return sorted(sub_expressions, key=lambda x: x.sort_key())   #UNCOMMENT THIS ONE
+    return sorted(sub_expressions, key=lambda x: (len(str(x)), str(x)))
+
+
+def check_valid_eqn(lhs, rhs):
+    """
+    Validates and modifies an equation (lhs = rhs) for solving.
+    
+    Checks:
+    1. Ensures the equation contains x. If not, returns invalid.
+    2. If x is on the rhs but not lhs, it swaps them.
+
+    Args:
+        lhs (sympy.Expr or int): Left-hand side of the equation.
+        rhs (sympy.Expr or int): Right-hand side of the equation.
+
+    Returns:
+        tuple: (bool, sympy.Expr, sympy.Expr) - Whether the equation is valid, and the modified (lhs, rhs).
+
+    Example:
+        >>> from sympy import symbols
+        >>> x, a = symbols('x a')
+        >>> is_valid_eqn(3, x + 1)
+        (True, x + 1, 3)
+
+        >>> is_valid_eqn(5, 2)
+        (False, 5, 2)
+    """
+
+    x = symbols('x')
+
+    # Check if lhs or rhs contains x
+    lhs_has_x = getattr(lhs, 'has', lambda x: False)(x)
+    rhs_has_x = getattr(rhs, 'has', lambda x: False)(x)
+
+    if not lhs_has_x and not rhs_has_x:
+        return False, lhs, rhs  # No x in the equation
+
+    if not lhs_has_x and rhs_has_x:
+        lhs, rhs = rhs, lhs  # Swap to ensure x is on lhs
+
+    return True, lhs, rhs
+
+
+def check_eqn_solved(lhs, rhs, main_eqn):
+    """Checks if the equation is solved efficiently."""
+    
+    x = symbols('x')
+    
+    # Solution must have form (lhs, rhs) = (x, something without x)
+    if lhs != x:
+        return False
+
+    # Check if rhs is a constant or does not contain x
+    if isinstance(rhs, (int, float, Number)) or x not in rhs.free_symbols:
+
+        # Substitute x = rhs into the main equation; should be zero
+        sol = main_eqn.subs(x, rhs)
+
+        # Fast checks before calling simplify
+        if sol == 0 or sol.is_zero:
+            return True
+
+        # Use a cheaper method before full simplify
+        if sol.expand() == 0:
+            return True
+
+        # Check for sqrts
+        if powdenest(sol, force=True) == 0:
+            return True
+
+        # Use cheaper method first
+        if ratsimp(sol) == 0:
+            return True
+
+        return False  # Avoid full simplify unless necessary
+    
+    return False
+
+
+
+######################################## 2D encoding #########################################
+
+def make_feature_dict_integer_2d(main_eqn):
+    """
+    Generates a dictionary that maps variables, constants, numbers, and operations 
+    in the given equation to a structured 2D encoding.
+
+    Encoding scheme:
+    - Variables: [0, index] (e.g., x → [0, 0])
+    - Constants/Symbols: [1, index] (e.g., a → [1, 0], b → [1, 1])
+    - Numbers/Special Constants: [2, index] (e.g., 0 → [2, 2], '-1' → [2, 0], 'I' → [2, 1])
+    - Operations: [3, index] (e.g., 'add' → [3, 0], 'pow' → [3, 1])
+
+    Example output:
+    ```
+    {
+        x: [0, 0],
+        b: [1, 0],
+        a: [1, 1],
+        '-1': [2, 0],
+        'I': [2, 1],
+        0: [2, 2],
+        1: [2, 3],
+        2: [2, 4],
+        3: [2, 5],
+        'add': [3, 0],
+        'pow': [3, 1],
+        'mul': [3, 2],
+        'sqrt': [3, 3]
+    }
+    ```
+
+    Args:
+        main_eqn (sympy expression): The mathematical equation to extract features from.
+
+    Returns:
+        dict: A dictionary mapping symbols and operations to their respective 2D encodings.
+    """
+
+    feature_dict = {}
+
+    # 0: relation, = is special
+    feature_dict['='] = [0,0]
+
+    # 1: Operations
+    operations = ['add', 'pow', 'mul', 'sqrt']
+    feature_dict.update({op: [1, idx] for idx, op in enumerate(operations)})
+
+    # 2: Variables
+    x = symbols('x')
+    variables = [x]
+    feature_dict.update({var: [2, idx] for idx, var in enumerate(variables)})
+
+    # 3: Constants/Symbols (excluding x)
+    symbols_const = sorted(main_eqn.free_symbols - {x}, key=str)  # Sort for consistency
+    feature_dict.update({sym: [3, idx] for idx, sym in enumerate(symbols_const)})
+
+    # 4: Numbers & Special Constants
+    special_constants = [I,E,pi,zoo]
+    feature_dict.update({num: [4, idx] for idx, num in enumerate(special_constants)})
+
+    # 5: Real numbers: added dynamically, so dont need in feature dict
+
+    return feature_dict
+
+
+def sympy_expression_to_list_2d(expr, feature_dict):
+    """
+    Convert a SymPy expression into a list of 2D encodings.
+    
+    Args:
+        expr (sympy.Expr): The symbolic expression to encode.
+        feature_dict (dict): Dictionary mapping symbols and operations to 2D encodings.
+
+    Returns:
+        List[List[int]]: A list of 2D encodings (without padding).
+    """
+
+    stack = [expr]
+    encoded_list = []
+
+    while stack:
+        node = stack.pop()
+
+        if node in feature_dict:
+            encoded_list.append(feature_dict[node])
+        elif isinstance(node, (int, float)):  # ✅ Handle numbers explicitly
+            category_real_numbers = 5
+            encoded_list.append([category_real_numbers, node])
+        elif node.is_Symbol:
+            encoded_list.append(feature_dict.get(node, [99, 99]))  # Use feature_dict encoding
+        else:
+            encoded_list.append(feature_dict.get(node.func.__name__.lower(), [99, 99]))  # Get encoding
+            stack.extend(reversed(node.args))  # Push children to stack
+
+    return encoded_list  # ✅ Returns list of valid encodings only (NO padding!)
+
+
+def integer_encoding_2d(lhs, rhs, feature_dict, max_length):
+    """
+    Convert symbolic expressions into 2D-encoded vectors with fixed-length padding.
+
+    Args:
+        lhs (sympy.Expr): Left-hand side expression.
+        rhs (sympy.Expr): Right-hand side expression.
+        feature_dict (dict): Dictionary mapping symbols and operations to 2D encodings.
+        max_length (int): Maximum allowed length for each expression.
+
+    Returns:
+        np.ndarray: A fixed-length 2D integer vector representing the encoded equation.
+    """
+
+    PAD_ID = [99, 99]  # Padding token
+
+    # ✅ Get valid encodings without padding
+    lhs_encoded = sympy_expression_to_list_2d(lhs, feature_dict)
+    rhs_encoded = sympy_expression_to_list_2d(rhs, feature_dict)
+
+    # ✅ Compute lengths
+    lhs_len, rhs_len = len(lhs_encoded), len(rhs_encoded)
+
+    # ✅ Fixed-size preallocation
+    vector = np.full((2 * max_length + 1, 2), PAD_ID, dtype=np.int32)
+
+    # ✅ Ensure lhs fits
+    lhs_trimmed = lhs_encoded[:max_length]  # Truncate if needed
+    lhs_actual_len = len(lhs_trimmed)
+
+    # ✅ Ensure rhs fits
+    rhs_trimmed = rhs_encoded[:max_length]  # Truncate if needed
+    rhs_actual_len = len(rhs_trimmed)
+
+    # ✅ Place values in the vector
+    vector[:lhs_actual_len] = lhs_trimmed
+    vector[lhs_actual_len] = feature_dict["="]  # Middle separator
+    vector[lhs_actual_len + 1 : lhs_actual_len + 1 + rhs_actual_len] = rhs_trimmed
+
+    complexity = 0
+    return vector, complexity
+
+
+###################################### Graph encoding #######################################
+
+
+def sympy_expression_to_graph(expr, feature_dict):
+    """
+    Convert a SymPy expression into a graph representation for Torch Geometric.
+    
+    Args:
+        expr (sympy.Expr): The symbolic expression to encode.
+        feature_dict (dict): Dictionary mapping symbols and operations to features.
+
+    Returns:
+        torch_geometric.data.Data: Graph representation of the expression.
+    """
+    
+    nodes = []  # List of node indices
+    edges = []  # List of (parent, child) edges
+    node_features = []  # Feature vector for each node
+    node_map = {}  # Maps SymPy nodes to indices
+
+    stack = [(expr, None)]  # (node, parent_index)
+    node_idx = 0
+
+    while stack:
+        node, parent_idx = stack.pop()
+
+        # Assign unique ID to each node
+        if node not in node_map:
+            node_map[node] = node_idx
+            node_idx += 1
+
+        cur_idx = node_map[node]
+        nodes.append(cur_idx)
+
+        # Get feature vector
+        if node in feature_dict:
+            node_features.append(feature_dict[node])
+        elif isinstance(node, (int, float, Integer, Float)):  # Handle numbers
+            node_features.append([5, node])  # Category 5 = real numbers
+        elif node.is_Symbol:
+            node_features.append(feature_dict.get(node, [99, 99]))  # Use feature_dict encoding
+        else:
+            node_features.append(feature_dict.get(node.func.__name__.lower(), [99, 99]))  # Get encoding
+
+        # Connect to parent (if applicable)
+        if parent_idx is not None:
+            edges.append((parent_idx, cur_idx))
+
+        # Push children to stack
+        if not type(node) in [int,float] and node.args:  
+            for child in reversed(node.args):
+                stack.append((child, cur_idx))
+
+    # Convert to Torch Tensors
+    edge_index = torch.tensor(edges, dtype=torch.long).T if edges else torch.empty((2, 0), dtype=torch.long)
+    x = torch.tensor(node_features, dtype=torch.float)
+
+    return Data(x=x, edge_index=edge_index)
+
+
+
+def graph_encoding(lhs, rhs, feature_dict, max_length):
+    """
+    Convert symbolic equations into graph representations with fixed-size encoding
+    and safely truncate overflow nodes/edges.
+    """
+
+    MAX_NODES, MAX_EDGES = 2*max_length + 1, 2*max_length + 1
+    MAX_EDGES = 2*MAX_NODES
+
+    # 1) Build raw graphs (LHS and RHS)
+    lhs_graph = sympy_expression_to_graph(lhs, feature_dict)
+    rhs_graph = sympy_expression_to_graph(rhs, feature_dict)
+
+    # 2) Insert "=" node between LHS and RHS
+    lhs_offset = lhs_graph.x.shape[0]
+    eq_node_feature = torch.tensor([[0, 0]], dtype=torch.float)  # "=" symbol
+    eq_node_idx = lhs_offset
+
+    # Shift RHS node indices by (lhs_offset + 1)
+    rhs_graph.edge_index += (lhs_offset + 1)
+
+    # Merge node features
+    x = torch.cat([lhs_graph.x, eq_node_feature, rhs_graph.x], dim=0)
+    
+    # Merge edges
+    edge_index = torch.cat([lhs_graph.edge_index, rhs_graph.edge_index], dim=1)
+
+    # Add edges connecting LHS-last-node -> "=" -> RHS-first-node
+    lhs_last_idx = lhs_offset - 1
+    rhs_start_idx = lhs_offset + 1
+    eq_edges = torch.tensor([
+        [lhs_last_idx, eq_node_idx],
+        [eq_node_idx,  rhs_start_idx]
+    ], dtype=torch.long)  # Shape is (2,2)
+
+    # Just concatenate them directly, both 2D:
+    edge_index = torch.cat([edge_index, eq_edges], dim=1)
+
+    # 3) Inspect raw node/edge count
+    raw_num_nodes = x.shape[0]
+    raw_num_edges = edge_index.shape[1]
+    #print("[graph_encoding] raw num_nodes:", raw_num_nodes)
+    #print("[graph_encoding] raw num_edges:", raw_num_edges)
+    #if edge_index.numel() > 0:
+        #print("[graph_encoding] raw edge_index:\n", edge_index)
+        #print("[graph_encoding] raw max node index in edges:", edge_index.max().item())
+
+    # 4) Remove edges that reference nodes >= MAX_NODES
+    #    (since we'll truncate node_features to the first MAX_NODES).
+    valid_edge_mask = (edge_index[0] < MAX_NODES) & (edge_index[1] < MAX_NODES)
+    edge_index = edge_index[:, valid_edge_mask]
+    truncated_num_edges = edge_index.shape[1]
+
+    # 5) Truncate node features if we have more than MAX_NODES
+    if raw_num_nodes > MAX_NODES:
+        x = x[:MAX_NODES]  # keep only the first MAX_NODES
+        truncated_num_nodes = MAX_NODES
+    else:
+        truncated_num_nodes = raw_num_nodes
+
+    # 6) Truncate edges if we have more than MAX_EDGES
+    if truncated_num_edges > MAX_EDGES:
+        edge_index = edge_index[:, :MAX_EDGES]
+        truncated_num_edges = MAX_EDGES
+
+    # 7) Now we pad up to MAX_NODES / MAX_EDGES
+    padded_x = torch.full((MAX_NODES, 2), 99, dtype=torch.float)
+    padded_x[:truncated_num_nodes] = x
+
+    padded_edge_index = torch.full((2, MAX_EDGES), 0, dtype=torch.long)
+    padded_edge_index[:, :truncated_num_edges] = edge_index
+
+    # 8) Create boolean masks indicating which entries are "real"
+    node_mask = torch.zeros(MAX_NODES, dtype=torch.bool)
+    node_mask[:truncated_num_nodes] = True
+
+    edge_mask = torch.zeros(MAX_EDGES, dtype=torch.bool)
+    edge_mask[:truncated_num_edges] = True
+
+    # Optional debug: final shapes
+    #print("[graph_encoding] final truncated_num_nodes:", truncated_num_nodes)
+    #print("[graph_encoding] final truncated_num_edges:", truncated_num_edges)
+    #if truncated_num_edges > 0:
+    #    print("[graph_encoding] final max node index in edges:", padded_edge_index[:,:truncated_num_edges].max().item())
+
+    # 9) Wrap into a dictionary
+    vec_dict = {
+        "node_features": padded_x.numpy(),
+        "edge_index": padded_edge_index.numpy(),
+        "node_mask": node_mask.numpy(),
+        "edge_mask": edge_mask.numpy(),
+    }
+
+    # Example complexity measure (not strictly needed)
+    complexity = 0
+
+    return vec_dict, complexity
+
+
+
+
+
+##############################################################################################################
+# Multi-Equation Feature Dictionary
+##############################################################################################################
+
+
+def make_feature_dict_multi(train_eqns, test_eqns, state_rep):
+    """Chooses the correct feature dictionary function based on `state_rep`."""
+    
+    if state_rep in {'integer_1d', 'graph_integer_1d'}:
+        return make_feature_dict_integer_1d_multi(train_eqns, test_eqns)
+    elif state_rep in {'integer_2d', 'graph_integer_2d'}:
+        return make_feature_dict_integer_2d_multi(train_eqns, test_eqns)
+    else:
+        raise ValueError(f"Unsupported encoding type: {state_rep}")
+
+
+def make_feature_dict_integer_1d_multi(train_eqns, test_eqns):
+    """
+    Generate a feature dictionary mapping symbols, numbers, and operations to unique integers.
+
+    Args:
+        train_eqns (list): List of training equations (SymPy expressions).
+        test_eqns (list): List of testing equations (SymPy expressions).
+
+    Returns:
+        dict: A dictionary mapping elements to integer encodings.
+    """
+
+    # Aggregate free symbols from ALL equations in train and test sets
+    free_symbols = set()
+    for eqn in train_eqns + test_eqns:
+        free_symbols.update(eqn.free_symbols)
+
+    # Special constants & numbers
+    special_constants = [-1, I, E, pi, zoo]
+    small_integers = list(range(4))  # e.g., 0,1,2,3
+    all_symbols = list(free_symbols) + special_constants + small_integers
+
+    # Initialize feature dictionary
+    x = symbols('x')
+    feature_dict = {'=': 0, x: 1}
+    ctr = 2
+
+    # Add all symbols
+    for symbol in all_symbols:
+        if symbol not in feature_dict:
+            feature_dict[symbol] = ctr
+            ctr += 1
+
+    # Add operations
+    operations = ['add', 'pow', 'mul', 'sqrt']
+    for idx, operation in enumerate(operations, start=-1):
+        feature_dict[operation] = idx
+
+    return feature_dict
+
+
+
+def make_feature_dict_integer_2d_multi(train_eqns, test_eqns):
+    """
+    Generates a dictionary that maps variables, constants, numbers, and operations 
+    in the given set of equations to a structured 2D encoding.
+
+    Encoding scheme:
+    - Variables: [2, index] (e.g., x → [2, 0])
+    - Constants/Symbols: [3, index] (e.g., a → [3, 0], b → [3, 1])
+    - Numbers/Special Constants: [4, index] (e.g., 0 → [4, 2], '-1' → [4, 0], 'I' → [4, 1])
+    - Operations: [1, index] (e.g., 'add' → [1, 0], 'pow' → [1, 1])
+    """
+
+    feature_dict = {}
+
+    # 0: Relation '='
+    feature_dict['='] = [0, 0]
+
+    # 1: Operations
+    operations = ['add', 'pow', 'mul', 'sqrt']
+    feature_dict.update({op: [1, idx] for idx, op in enumerate(operations)})
+
+    # 2: Variables (Ensure 'x' is always present)
+    x = symbols('x')
+    feature_dict[x] = [2, 0]
+
+    # 3: Constants/Symbols (excluding x)
+    symbols_const = set()
+    for eqn in train_eqns + test_eqns:
+        symbols_const.update(eqn.free_symbols)
+
+    # Remove 'x' since it's categorized separately
+    symbols_const.discard(x)
+    feature_dict.update({sym: [3, idx] for idx, sym in enumerate(sorted(symbols_const, key=str))})
+
+    # 4: Numbers & Special Constants
+    special_constants = [-1, I, E, pi, zoo]
+    feature_dict.update({num: [4, idx] for idx, num in enumerate(special_constants)})
+
+    return feature_dict
+
+
+
+
+################################# MULTIEQNS ###############################################
+
+
+# def load_train_test_equations(dirn, level):
+#     """
+#     Loads train and test equations for a given level from the stored text files.
+#     Returns lists of sympified equations.
+#     """
+#     level_dir = os.path.join(dirn, f"level{level}")
+
+#     train_eqns_path = os.path.join(level_dir, "train_eqns.txt")
+#     test_eqns_path = os.path.join(level_dir, "test_eqns.txt")
+
+#     if not os.path.exists(train_eqns_path) or not os.path.exists(test_eqns_path):
+#         raise FileNotFoundError(f"Train or test equation file not found for level {level} in {level_dir}")
+
+#     # Read and sympify each equation
+#     with open(train_eqns_path, "r") as f:
+#         train_eqns = [sympify(line.strip()) for line in f.readlines()]
+
+#     with open(test_eqns_path, "r") as f:
+#         test_eqns = [sympify(line.strip()) for line in f.readlines()]
+
+#     return train_eqns, test_eqns
+
+
+
+def load_train_test_equations(dirn, level, generalization="shallow"):
+    """
+    Loads train and test equations for a given level from the stored text files.
+    Supports both "shallow" and "deep" generalization.
+    
+    Parameters:
+        dirn (str): Base directory where equation templates are stored.
+        level (int): The level of equations to load.
+        generalization (str): Either "shallow" or "deep" to load respective datasets.
+
+    Returns:
+        train_eqns (list): List of sympified train equations.
+        test_eqns (list): List of sympified test equations.
+    """
+    if generalization not in ["shallow", "lexical", "structural", "deep", "random"]:
+        raise ValueError(f"Invalid generalization type '{generalization}'. Choose 'shallow' or 'deep'.")
+
+    # Construct path based on generalization type
+    level_dir = os.path.join(dirn, generalization, f"level{level}")
+
+    train_eqns_path = os.path.join(level_dir, "train_eqns.txt")
+    test_eqns_path = os.path.join(level_dir, "test_eqns.txt")
+
+    if not os.path.exists(train_eqns_path) or not os.path.exists(test_eqns_path):
+        raise FileNotFoundError(f"Train or test equation file not found for level {level} in {level_dir}")
+
+    # Read and sympify each equation
+    with open(train_eqns_path, "r") as f:
+        train_eqns = [sympify(line.strip()) for line in f.readlines()]
+
+    with open(test_eqns_path, "r") as f:
+        test_eqns = [sympify(line.strip()) for line in f.readlines()]
+
+    return train_eqns, test_eqns
+