content: # Copyright (c) Meta Platforms, Inc. and affiliates.
  #
  # This source code is licensed under the Apache License, Version 2.0
  # found in the LICENSE file in the root directory of this source tree.
  
  import warnings
  
  import torch.nn.functional as F
  
  
  def resize(input, size=None, scale_factor=None, mode="nearest", align_corners=None, warning=False):
      if warning:
          if size is not None and align_corners:
              input_h, input_w = tuple(int(x) for x in input.shape[2:])
              output_h, output_w = tuple(int(x) for x in size)
              if output_h > input_h or output_w > output_h:
                  if (
                      (output_h > 1 and output_w > 1 and input_h > 1 and input_w > 1)
                      and (output_h - 1) % (input_h - 1)
                      and (output_w - 1) % (input_w - 1)
                  ):
                      warnings.warn(
                          f"When align_corners={align_corners}, "
                          "the output would more aligned if "
                          f"input size {(input_h, input_w)} is `x+1` and "
                          f"out size {(output_h, output_w)} is `nx+1`"
                      )
      return F.interpolate(input, size, scale_factor, mode, align_corners)
  