class_docstr: Depth to Space feature map upsampling that produces a spatially larger feature map but with smaller number of
  channels. Of the input channels is not divisble by scale_factor^2, an additional 1x1 convolution will be
  applied.
  
  https://github.com/pytorch/pytorch/pull/5429
  https://arxiv.org/ftp/arxiv/papers/1707/1707.02937.pdf
code: def forward(
      self,
      x: Tensor,
      output_size: Union[Tuple[int, int], torch.Size] = None,
  ) -> Tensor:
      x = self.shuffle(self.conv(x))
      return x
  
imports: from enum import Enum
  from math import hypot
  from typing import Optional, List, Tuple, Union, Type
  import torch
  from torch import nn, Tensor
label_desc: Use image node_id 2 for calling the PixelShuffle obj's underlying member method code with example usage: obj.forward(x, output_size) and returns: x