code: def setup(self):
      # constructing the global encoder based on the observation_space of the first
      # module
      module_specs = self.config.modules
      module_spec = next(iter(module_specs.values()))
      global_dim = module_spec.observation_space["global"].shape[0]
      hidden_dim = module_spec.model_config_dict["fcnet_hiddens"][0]
      shared_encoder = tf.keras.Sequential(
          [
              tf.keras.Input(shape=(global_dim,)),
              tf.keras.layers.ReLU(),
              tf.keras.layers.Dense(hidden_dim),
          ]
      )
  
      for module_id, module_spec in module_specs.items():
          self._rl_modules[module_id] = module_spec.module_class(
              encoder=shared_encoder,
              local_dim=module_spec.observation_space["local"].shape[0],
              hidden_dim=hidden_dim,
              action_dim=module_spec.action_space.n,
          )
  
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 2 for calling the BCTfMultiAgentModuleWithSharedEncoder obj's underlying member method code with example usage: obj.setup() without return types