class_docstr: Custom model that adds an imitation loss on top of the policy loss.
class_docstr_tok: ["Custom","model","that","adds","an","imitation","loss","on","top","of","the","policy","loss","."]
code: def forward(self, input_dict, state, seq_lens):
      # Delegate to our FCNet.
      return self.fcnet(input_dict, state, seq_lens)
  
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 2 for calling the CustomLossModel obj's underlying member method code with example usage: obj.forward(input_dict, state, seq_lens) and returns: self