code: def _forward(self, inputs: torch.Tensor, **kwargs) -> torch.Tensor:
      out = self.initial_dense(inputs)
      # Reshape to initial 3D (image-like) format to enter CNN transpose stack.
      out = out.reshape((-1,) + tuple(self.config.initial_image_dims))
      out = self.cnn_transpose_net(out)
      # Add 0.5 to center (always non-activated, non-normalized) outputs more
      # around 0.0.
      return out + 0.5
  
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 4 for calling the TorchCNNTransposeHead obj's underlying member method code with example usage: obj._forward(inputs) and returns: unknown