code: def get_input_specs(self) -> Optional[Spec]:
      return SpecDict(
          {
              SampleBatch.OBS: TensorSpec(
                  "b, w, h, c",
                  w=self.config.input_dims[0],
                  h=self.config.input_dims[1],
                  c=self.config.input_dims[2],
                  framework="torch",
              ),
          }
      )
  
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 2 for calling the TorchCNNEncoder obj's underlying member method code with example usage: obj.get_input_specs() and returns: SpecDict