code: def create_random_augment(
      input_size,
      auto_augment=None,
      interpolation="bilinear",
  ):
      """
      Get video randaug transform.
  
      Args:
          input_size: The size of the input video in tuple.
          auto_augment: Parameters for randaug. An example:
              "rand-m7-n4-mstd0.5-inc1" (m is the magnitude and n is the number
              of operations to apply).
          interpolation: Interpolation method.
      """
      if isinstance(input_size, tuple):
          img_size = input_size[-2:]
      else:
          img_size = input_size
  
      if auto_augment:
          assert isinstance(auto_augment, str)
          if isinstance(img_size, tuple):
              img_size_min = min(img_size)
          else:
              img_size_min = img_size
          aa_params = {"translate_const": int(img_size_min * 0.45)}
          if interpolation and interpolation != "random":
              aa_params["interpolation"] = _pil_interp(interpolation)
          if auto_augment.startswith("rand"):
              return transforms.Compose(
                  [rand_augment_transform(auto_augment, aa_params)]
              )
      raise NotImplementedError
  
imports: import logging
  import math
  import numpy
  import random
  import torch
  import torchvision
  import torchvision.transforms.functional
  from PIL import Image, ImageFilter
  from scipy.ndimage import gaussian_filter
  from torchvision import transforms
  from .rand_augment import rand_augment_transform
  from .random_erasing import RandomErasing
label_desc: Use image node_id 19 for calling a global function with example usage: create_random_augment(input_size, auto_augment, interpolation) and returns: transforms