code: def rand_cutout(image, size, center_bias=False, center_focus=2):
      width = image.shape[-1]
      min_offset = 0
      max_offset = width - size
      if center_bias:
          # sample around image center
          center = max_offset / 2
          std = center / center_focus
          offset_x = int(random.gauss(mu=center, sigma=std))
          offset_y = int(random.gauss(mu=center, sigma=std))
          # resample uniformly if over boundaries
          offset_x = (
              random.randint(min_offset, max_offset)
              if (offset_x > max_offset or offset_x < min_offset)
              else offset_x
          )
          offset_y = (
              random.randint(min_offset, max_offset)
              if (offset_y > max_offset or offset_y < min_offset)
              else offset_y
          )
      else:
          offset_x = random.randint(min_offset, max_offset)
          offset_y = random.randint(min_offset, max_offset)
      cutout = image[
          :, :, offset_x : offset_x + size, offset_y : offset_y + size
      ]
      return cutout
  
imports: import os
  import subprocess
  import sys
  import random
  from datetime import datetime
  from pathlib import Path
  import torch
  import torch.nn.functional
  from siren_pytorch import SirenNet, SirenWrapper
  from torch import nn
  from torch.cuda.amp import GradScaler, autocast
  from torch_optimizer import DiffGrad, AdamP
  import numpy
  from PIL import Image
  from imageio import imread, mimsave
  import torchvision.transforms
  from tqdm import trange, tqdm
  from .clip import load, tokenize
label_desc: Use image node_id 4 for calling a global function with example usage: rand_cutout(image, size, center_bias, center_focus) and returns: cutout