code: def __init__(self, in_channels: int, scale_factor=2):
      if scale_factor != 2:
          raise NotImplementedError(
              f"Scale factor other than 2 is not implemented. Got scale factor of {scale_factor}"
          )
      super().__init__()
      n = scale_factor * scale_factor
      self.in_channels = in_channels
      self.out_channels = in_channels // n
      self.conv = nn.ConvTranspose2d(
          in_channels,
          in_channels // n,
          kernel_size=3,
          padding=1,
          stride=scale_factor,
          output_padding=1,
      )
      self.residual = BilinearAdditiveUpsample2d(
          in_channels, scale_factor=scale_factor
      )
  
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 1 to create a new ResidualDeconvolutionUpsample2d object from inherited base classes: AbstractResizeLayer with example: obj = ResidualDeconvolutionUpsample2d(in_channels, scale_factor)