code: def get_input_specs(self) -> Optional[Spec]:
      return TensorSpec(
          "b, d", d=self.config.input_dims[0], framework="torch"
      )
  
imports: import functools
  from typing import Optional
  import numpy
  from ray.rllib.core.models.base import Model
  from ray.rllib.core.models.configs import CNNTransposeHeadConfig, FreeLogStdMLPHeadConfig, MLPHeadConfig
  from ray.rllib.core.models.specs.checker import SpecCheckingError
  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.torch.base import TorchModel
  from ray.rllib.core.models.torch.primitives import TorchCNNTranspose, TorchMLP
  from ray.rllib.models.utils import get_initializer_fn
  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 TorchMLPHead obj's underlying member method code with example usage: obj.get_input_specs() and returns: TensorSpec