class_docstr: This module resizes a list of image tensors to the given size.
  
  Additionally, also returns the original image sizes for further post-processing.
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docstr: Args:
      size: images will be resized to this value. If a 2-integer tuple is given, it is interpreted as
          (height, width).
      interpolation_mode: interpolation mode for image resizing. Supported values: ``nearest``, ``bilinear``,
          ``bicubic``, ``area``, and ``nearest-exact``.
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code: def __init__(
      self, size: tuple[int, int], interpolation_mode: str = "bilinear"
  ) -> None:
      """
      Args:
          size: images will be resized to this value. If a 2-integer tuple is given, it is interpreted as
              (height, width).
          interpolation_mode: interpolation mode for image resizing. Supported values: ``nearest``, ``bilinear``,
              ``bicubic``, ``area``, and ``nearest-exact``.
      """
      super().__init__()
      self.size = size
      self.interpolation_mode = interpolation_mode
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from enum import Enum
  from typing import Optional
  import torch
  from kornia.core import Module, Tensor, concatenate
  from kornia.core.check import KORNIA_CHECK_SHAPE
  from kornia.image.base import ImageSize
label_desc: Use image node_id 1 to create a new ResizePreProcessor object from inherited base classes: Module with example: obj = ResizePreProcessor(size, interpolation_mode)