code: def forward(self, input_dict, state, seq_lens):
      try:
          x = input_dict["obs"]
      except KeyError:
          x = input_dict["observation"]
      x = self.shared_layers(x)
      # actor outputs
      logits = self.actor_layers(x)
  
      # compute value
      self._value_out = self.critic_layers(x)
      return logits, None
  
file_path: ray/rllib_contrib/leela_chess_zero/examples/custom_model.py
imports: import torch.nn
  from ray.rllib.algorithms.alpha_zero.models.custom_torch_models import ActorCriticModel, Flatten
label_desc: Use image node_id 2 for calling the DenseModel obj's underlying member method code with example usage: obj.forward(input_dict, state, seq_lens) and returns: logits,  None