class_docstr: A model containing a CNNTranspose with N Conv2DTranspose layers.
  
  All layers share the same activation function, bias setup (use bias or not),
  and LayerNormalization setup (use layer normalization or not), except for the last
  one, which is never activated and never layer norm'd.
  
  Note that there is no reshaping/flattening nor an additional dense layer at the
  beginning or end of the stack. The input as well as output of the network are 3D
  tensors of dimensions [width x height x num output filters].
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code: def forward(self, inputs):
      # Permute b/c data comes in as [B, dim, dim, channels]:
      out = inputs.permute(0, 3, 1, 2)
      out = self.cnn_transpose(out.type(self.expected_input_dtype))
      return out.permute(0, 2, 3, 1)
  
file_path: ray/python/ray/rllib/core/models/torch/primitives.py
imports: from typing import Callable, Dict, List, Optional, Union, Tuple
  from ray.rllib.core.models.torch.utils import Stride2D
  from ray.rllib.models.torch.misc import same_padding, same_padding_transpose_after_stride, valid_padding
  from ray.rllib.models.utils import get_activation_fn, get_initializer_fn
  from ray.rllib.utils.framework import try_import_torch
label_desc: Use image node_id 2 for calling the TorchCNNTranspose obj's underlying member method code with example usage: obj.forward(inputs) and returns: out