code: def cutmix_bbox_and_lam(
      img_shape, lam, ratio_minmax=None, correct_lam=True, count=None
  ):
      """Generate bbox and apply lambda correction."""
      if ratio_minmax is not None:
          yl, yu, xl, xu = rand_bbox_minmax(
              img_shape, ratio_minmax, count=count
          )
      else:
          yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
      if correct_lam or ratio_minmax is not None:
          bbox_area = (yu - yl) * (xu - xl)
          lam = 1.0 - bbox_area / float(img_shape[-2] * img_shape[-1])
      return (yl, yu, xl, xu), lam
  
imports: import collections.abc
  from typing import Any, Dict, Optional
  import numpy
  import torch
  from classy_vision.generic.util import convert_to_one_hot
  from torch.distributions.beta import Beta
  from vissl.data.collators import register_collator
  from .moco_collator import moco_collator
  from .simclr_collator import simclr_collator
label_desc: Use image node_id 8 for calling a global function with example usage: cutmix_bbox_and_lam(img_shape, lam, ratio_minmax, correct_lam, count) and returns: yl,  yu,  xl,  xu,  lam