code: def get_random_bounding_box(
      image_shape: Tuple[int, int],
      ratio: jnp.ndarray,
      rng: Any,
      margin: float = 0.0,
  ) -> Tuple[int, int, int, int]:
      """Returns a random bounding box for Cutmix.
  
      Based on the implementation in timm:
      https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/mixup.py
  
      Args:
        image_shape: The shape of the image, specified as [height, width].
        ratio: Ratio of the input height/width to use as the maximum dimensions of
          the randomly sampled bounding box.
        rng: JAX rng key.
        margin: Percentage of bounding box dimension to enforce as the margin.
          This reduces the amount of the bounding box outside the image.
  
      Returns:
        The bounding box parameterised as y_min, y_max, x_min, x_max.
      """
      img_h, img_w = image_shape
      cut_h, cut_w = (img_h * ratio).astype(int), (
          img_w * ratio
      ).astype(int)
      margin_y, margin_x = (margin * cut_h).astype(int), (
          margin * cut_w
      ).astype(int)
      rngx, rngy = jax.random.split(rng)
      cy = jax.random.randint(rngy, [1], 0 + margin_y, img_h - margin_y)
      cx = jax.random.randint(rngx, [1], 0 + margin_x, img_w - margin_x)
  
      y_min = jnp.clip(cy - cut_h // 2, 0, img_h)[0]
      y_max = jnp.clip(cy + cut_h // 2, 0, img_h)[0]
      x_min = jnp.clip(cx - cut_w // 2, 0, img_w)[0]
      x_max = jnp.clip(cx + cut_w // 2, 0, img_w)[0]
      return y_min, y_max, x_min, x_max
  
imports: import functools
  from typing import Any, Callable, Dict, Optional, Tuple, Union
  from absl import logging
  from clu import platform
  import flax.linen
  import jax
  import jax.numpy
  import matplotlib.pyplot
  import numpy
  from scenic.model_lib.base_models import model_utils
  from scenic.model_lib.layers import nn_ops
  from scenic.train_lib import train_utils
  from scenic.train_lib_deprecated import train_utils
label_desc: Use image node_id 3 for calling a global function with example usage: get_random_bounding_box(image_shape, ratio, rng, margin) and returns: y_min,  y_max,  x_min,  x_max