class_docstr: Basic class for all upsampling blocks. It forces the upsample block to have a specific
  signature of forward method.
docstr: :param x: Input feature map to resize
  :param output_size: Target output size. This serves as a hint for the upsample block.
  :return:
docstr_tok: [":","param","x",":","Input","feature","map","to","resize",":","param","output_size",":","Target","output","size",".","This","serves","as","a","hint","for","the","upsample","block",".",":","return",":"]
code: def forward(
      self, x: Tensor, output_size: Union[Tuple[int, int], torch.Size]
  ) -> Tensor:
      """
  
      :param x: Input feature map to resize
      :param output_size: Target output size. This serves as a hint for the upsample block.
      :return:
      """
      raise NotImplementedError
  
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 for calling the AbstractResizeLayer obj's underlying member method code with example usage: obj.forward(x, output_size) without return types