code: def setup(self):
      input_dim = self.config.observation_space.shape[0]
      hidden_dim = self.config.model_config_dict["fcnet_hiddens"][0]
      output_dim = self.config.action_space.n
      layers = []
  
      layers.append(tf.keras.Input(shape=(input_dim,)))
      layers.append(tf.keras.layers.ReLU())
      layers.append(tf.keras.layers.Dense(hidden_dim))
      layers.append(tf.keras.layers.ReLU())
      layers.append(tf.keras.layers.Dense(output_dim))
  
      self.policy = tf.keras.Sequential(layers)
      self._input_dim = input_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 2 for calling the DiscreteBCTFModule obj's underlying member method code with example usage: obj.setup() without return types