class_docstr: PyTorch version of the CustomLossModel above.
class_docstr_tok: ["PyTorch","version","of","the","CustomLossModel","above","."]
code: def __init__(
      self,
      obs_space,
      action_space,
      num_outputs,
      model_config,
      name,
      input_files,
  ):
      super().__init__(
          obs_space, action_space, num_outputs, model_config, name
      )
      nn.Module.__init__(self)
  
      self.input_files = input_files
      # Create a new input reader per worker.
      self.reader = JsonReader(self.input_files)
      self.fcnet = TorchFC(
          self.obs_space,
          self.action_space,
          num_outputs,
          model_config,
          name="fcnet",
      )
  
imports: import numpy
  from ray.rllib.models.modelv2 import ModelV2, restore_original_dimensions
  from ray.rllib.models.tf.tf_action_dist import Categorical
  from ray.rllib.models.tf.tf_modelv2 import TFModelV2
  from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.models.torch.fcnet import FullyConnectedNetwork
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.offline import JsonReader
label_desc: Use image node_id 1 to create a new TorchCustomLossModel object from inherited base classes: TorchModelV2, nn with example: obj = TorchCustomLossModel(obs_space, action_space, num_outputs, model_config, name, input_files)