code: def inception_crop(image, resize_size=224, area_min=20, area_max=80):
      """Random crop input image."""
      begin, size, _ = tf.image.sample_distorted_bounding_box(
          tf.shape(image),
          tf.zeros([0, 0, 4], tf.float32),
          area_range=(area_min / 100, area_max / 100),
          min_object_covered=0,  # Don't enforce a minimum area.
          use_image_if_no_bounding_boxes=True,
      )
      crop = tf.slice(image, begin, size)
      # Unfortunately, the above operation loses the depth-dimension. So we need
      # to restore it the manual way.
      crop.set_shape([None, None, image.shape[-1]])
      if resize_size:
          crop = tf.cast(
              tf.image.resize(crop, [resize_size, resize_size]),
              image.dtype,
          )
      return crop
  
imports: import functools
  from typing import Optional
  from absl import logging
  from flax import jax_utils
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.dataset_lib.big_transfer import builder
  from scenic.dataset_lib.big_transfer import registry
  from scenic.dataset_lib import web_image_text_dataset
  from scenic.projects.knowledge_visual_language.data import data_utils
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: inception_crop(image, resize_size, area_min, area_max) and returns: crop