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
  
      self.policy = nn.Sequential(
          nn.Linear(input_dim, hidden_dim),
          nn.ReLU(),
          nn.Linear(hidden_dim, output_dim),
      )
  
      self.input_dim = input_dim
  
imports: from typing import Any, Mapping
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.core.rl_module.rl_module import RLModule, RLModuleConfig
  from ray.rllib.models.torch.torch_distributions import TorchCategorical
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModuleConfig, MultiAgentRLModule
  from ray.rllib.core.rl_module.torch.torch_rl_module import TorchRLModule
  from ray.rllib.core.models.specs.typing import SpecType
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.nested_dict import NestedDict
label_desc: Use image node_id 2 for calling the DiscreteBCTorchModule obj's underlying member method code with example usage: obj.setup() without return types