code: def compute_loss_for_module(
      self,
      *,
      module_id: ModuleID,
      config: "AlgorithmConfig",
      batch: NestedDict,
      fwd_out: Mapping[str, TensorType],
  ) -> Mapping[str, Any]:
      action_dist_inputs = fwd_out[SampleBatch.ACTION_DIST_INPUTS]
      action_dist_class = self._module[
          module_id
      ].get_train_action_dist_cls()
      action_dist = action_dist_class.from_logits(action_dist_inputs)
      loss = -torch.mean(action_dist.logp(batch[SampleBatch.ACTIONS]))
  
      return loss
  
file_path: ray/python/ray/rllib/core/testing/torch/bc_learner.py
imports: import torch
  from typing import Any, Mapping, TYPE_CHECKING
  from ray.rllib.core.learner.torch.torch_learner import TorchLearner
  from ray.rllib.core.testing.testing_learner import BaseTestingLearner
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.nested_dict import NestedDict
  from ray.rllib.utils.typing import ModuleID, TensorType
label_desc: Use image node_id 1 for calling the BCTorchLearner obj's underlying member method code with example usage: obj.compute_loss_for_module() and returns: loss