code: def _get_param_spatial_crop(
      scale,
      ratio,
      height,
      width,
      num_repeat=10,
      log_scale=True,
      switch_hw=False,
  ):
      """
      Given scale, ratio, height and width, return sampled coordinates of the videos.
      """
      for _ in range(num_repeat):
          area = height * width
          target_area = random.uniform(*scale) * area
          if log_scale:
              log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
              aspect_ratio = math.exp(random.uniform(*log_ratio))
          else:
              aspect_ratio = random.uniform(*ratio)
  
          w = int(round(math.sqrt(target_area * aspect_ratio)))
          h = int(round(math.sqrt(target_area / aspect_ratio)))
  
          if np.random.uniform() < 0.5 and switch_hw:
              w, h = h, w
  
          if 0 < w <= width and 0 < h <= height:
              i = random.randint(0, height - h)
              j = random.randint(0, width - w)
              return i, j, h, w
  
      # Fallback to central crop
      in_ratio = float(width) / float(height)
      if in_ratio < min(ratio):
          w = width
          h = int(round(w / min(ratio)))
      elif in_ratio > max(ratio):
          h = height
          w = int(round(h * max(ratio)))
      else:  # whole image
          w = width
          h = height
      i = (height - h) // 2
      j = (width - w) // 2
      return i, j, h, w
  
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 16 for calling a global function with example usage: _get_param_spatial_crop(scale, ratio, height, width, num_repeat, log_scale, switch_hw) and returns: i,  j,  h,  w, i,  j,  h,  w