code: def rand_bbox(size, lam, scale=1):
      w = size[1] // scale
      h = size[2] // scale
      cut_rat = np.sqrt(1.0 - lam)
      cut_w = np.int64(w * cut_rat)
      cut_h = np.int64(h * cut_rat)
  
      # uniform
      cx = np.random.randint(w)
      cy = np.random.randint(h)
  
      bbx1 = np.clip(cx - cut_w // 2, 0, w)
      bby1 = np.clip(cy - cut_h // 2, 0, h)
      bbx2 = np.clip(cx + cut_w // 2, 0, w)
      bby2 = np.clip(cy + cut_h // 2, 0, h)
  
      return bbx1, bby1, bbx2, bby2
  
imports: import torch
  from torch import nn
  from timm.models.layers import DropPath
  import numpy
  from towhee.models.wave_vit.wave_vit_utils import DWT2D, IDWT2D
  from towhee.models.utils.init_vit_weights import init_vit_weights
  from towhee.models.layers.mlp import Mlp
label_desc: Use image node_id 1 for calling a global function with example usage: rand_bbox(size, lam, scale) and returns: bbx1,  bby1,  bbx2,  bby2