class_docstr: An actor-critic encoder for torch.
code: def __init__(self, config: ActorCriticEncoderConfig) -> None:
      TorchModel.__init__(self, config)
      ActorCriticEncoder.__init__(self, config)
  
imports: from typing import Optional
  import tree
  from ray.rllib.core.models.base import Encoder, ActorCriticEncoder, StatefulActorCriticEncoder, STATE_IN, STATE_OUT, ENCODER_OUT
  from ray.rllib.core.models.base import Model, tokenize
  from ray.rllib.core.models.configs import ActorCriticEncoderConfig, CNNEncoderConfig, MLPEncoderConfig, RecurrentEncoderConfig
  from ray.rllib.core.models.specs.specs_base import Spec
  from ray.rllib.core.models.specs.specs_base import TensorSpec
  from ray.rllib.core.models.specs.specs_dict import SpecDict
  from ray.rllib.core.models.torch.base import TorchModel
  from ray.rllib.core.models.torch.primitives import TorchMLP, TorchCNN
  from ray.rllib.models.utils import get_initializer_fn
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
label_desc: Use image node_id 1 to create a new TorchActorCriticEncoder object from inherited base classes: TorchModel, ActorCriticEncoder with example: obj = TorchActorCriticEncoder(config)