code: def __init__(self, encoder, local_dim, hidden_dim, action_dim):
      super().__init__()
  
      self.encoder = encoder
      self.policy_head = tf.keras.Sequential(
          [
              tf.keras.layers.Dense(
                  hidden_dim + local_dim,
                  input_shape=(hidden_dim + local_dim,),
                  activation="relu",
              ),
              tf.keras.layers.Dense(hidden_dim, activation="relu"),
              tf.keras.layers.Dense(action_dim),
          ]
      )
  
imports: import tensorflow
  from typing import Any, Mapping
  from ray.rllib.core.rl_module.rl_module import RLModule, RLModuleConfig
  from ray.rllib.models.tf.tf_distributions import TfCategorical
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModule, MultiAgentRLModuleConfig
  from ray.rllib.core.rl_module.tf.tf_rl_module import TfRLModule
  from ray.rllib.core.models.specs.typing import SpecType
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.nested_dict import NestedDict
label_desc: Use image node_id 1 to create a new BCTfRLModuleWithSharedGlobalEncoder object from inherited base classes: TfRLModule with example: obj = BCTfRLModuleWithSharedGlobalEncoder(encoder, local_dim, hidden_dim, action_dim)