class_docstr: A video clip transform that is often useful for trainig data.
  
  Given a size range, randomly choose a size. Rescale the clip so that
  its short edge equals to the chosen size. Then randomly crop the video
  clip with the specified size.
  Such training data augmentation is used in VGG net
  (https://arxiv.org/abs/1409.1556).
  Also see reference implementation `Kinetics.spatial_sampling` in SlowFast
      codebase.
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docstr: The constructor method of VideoClipRandomResizeCrop class.
  
  Args:
      crop_size: int or 2-tuple as the expected output crop_size (height, width)
      size_range: the min- and max size
      interpolation_mode: Default: "bilinear"
docstr_tok: ["The","constructor","method","of","VideoClipRandomResizeCrop","class",".","Args",":","crop_size",":","int","or","2-tuple","as","the","expected","output","crop_size","(","height",",","width",")","size_range",":","the","min-","and","max","size","interpolation_mode",":","Default",":","``","bilinear","''"]
code: def __init__(
      self,
      crop_size: Union[int, List[int]],
      size_range: List[int],
      interpolation_mode: str = "bilinear",
  ):
      """The constructor method of VideoClipRandomResizeCrop class.
  
      Args:
          crop_size: int or 2-tuple as the expected output crop_size (height, width)
          size_range: the min- and max size
          interpolation_mode: Default: "bilinear"
  
      """
      if isinstance(crop_size, tuple):
          assert (
              len(crop_size) == 2
          ), "crop_size should be tuple (height, width)"
          self.crop_size = crop_size
      else:
          self.crop_size = (crop_size, crop_size)
  
      self.interpolation_mode = interpolation_mode
      self.size_range = size_range
  
imports: import random
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  import torchvision.transforms
  import torchvision.transforms._transforms_video
  from .None import build_transforms, ClassyTransform, register_transform
  from .util import ApplyTransformToKey, ImagenetConstants, TupleToMapTransform
label_desc: Use image node_id 1 to create a new VideoClipRandomResizeCrop object from inherited base classes: ClassyTransform with example: obj = VideoClipRandomResizeCrop(crop_size, size_range, interpolation_mode)