code: def test_forward_train(self):
      bsize = 1024
      env = gym.make("CartPole-v1")
      module = DiscreteBCTorchModule(
          config=RLModuleConfig(
              env.observation_space,
              env.action_space,
              model_config_dict={"fcnet_hiddens": [32]},
          )
      )
  
      obs_shape = env.observation_space.shape
      obs = torch.randn((bsize,) + obs_shape)
      actions = torch.stack(
          [
              torch.tensor(env.action_space.sample())
              for _ in range(bsize)
          ]
      )
      output = module.forward_train({"obs": obs})
  
      self.assertIsInstance(output, Mapping)
      self.assertIn(SampleBatch.ACTION_DIST_INPUTS, output)
  
      action_dist_inputs = output[SampleBatch.ACTION_DIST_INPUTS]
      action_dist_class = module.get_train_action_dist_cls()
      action_dist = action_dist_class.from_logits(action_dist_inputs)
  
      loss = -action_dist.logp(actions.view(-1)).mean()
      loss.backward()
  
      # check that all neural net parameters have gradients
      for param in module.parameters():
          self.assertIsNotNone(param.grad)
  
imports: import tempfile
  import unittest
  from typing import Mapping
  import gc
  import gymnasium
  import torch
  from ray.rllib.utils.torch_utils import _dynamo_is_available
  from ray.rllib.core.rl_module.rl_module import RLModuleConfig
  from ray.rllib.core.rl_module.torch import TorchRLModule
  from ray.rllib.core.rl_module.torch.torch_compile_config import TorchCompileConfig
  from ray.rllib.core.testing.torch.bc_module import DiscreteBCTorchModule
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 2 for calling the TestRLModule obj's underlying member method code with example usage: obj.test_forward_train() without return types