code: def _forward(self, inputs: dict, **kwargs) -> dict:
      return {ENCODER_OUT: self.net(inputs[SampleBatch.OBS])}
  
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 4 for calling the TorchCNNEncoder obj's underlying member method code with example usage: obj._forward(inputs) and returns: dict