code: def crop(features, region):
      """Crop the image + bbox (+ mask) to region.
  
      WARNING! Only use during train. In eval mode the original_size would need to
      be updated somehow.
  
      Args:
        features: DETR decoded input features.
        region: (i, j, h, w) tuple of the region to be cropped.
  
      Returns:
        Cropped features dictionary.
      """
      image = features["inputs"]
      target = features["label"]
      i, j, h, w = region
  
      cropped_image = image[i : i + h, j : j + w, :]
      features["inputs"] = cropped_image
  
      target["size"] = tf.stack([h, w])
  
      fields = ["labels", "area", "is_crowd", "objects/id"]
  
      if "boxes" in target:
          boxes = target["boxes"]
          cropped_boxes = boxes - tf_float(
              tf.expand_dims(tf.stack([j, i, j, i]), axis=0)
          )
          cropped_boxes = tf.minimum(
              tf.reshape(cropped_boxes, [-1, 2, 2]),
              tf.reshape(tf_float(tf.stack([w, h])), [1, 1, 2]),
          )
          cropped_boxes = tf.clip_by_value(cropped_boxes, 0, 1000000)
          target["boxes"] = tf.reshape(cropped_boxes, [-1, 4])
          fields.append("boxes")
  
          if "area" in target:
              area = tf.reduce_prod(
                  cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :],
                  axis=1,
              )
              target["area"] = area
  
      if "masks" in target:
          # TODO(aravindhm): should we update the area here if there are no boxes?
          target["masks"] = target["masks"][
              ..., i : i + h, j : j + w, :
          ]
          fields.append("masks")
  
      # Removes elements for which the boxes or masks that have zero area.
      if "boxes" in target or "masks" in target:
          if "boxes" in target:
              cropped_boxes = tf.reshape(target["boxes"], [-1, 2, 2])
              keep = tf.logical_and(
                  cropped_boxes[:, 1, 0] > cropped_boxes[:, 0, 0],
                  cropped_boxes[:, 1, 1] > cropped_boxes[:, 0, 1],
              )
          else:
              keep = tf.reduce_any(
                  tf.not_equal(target["masks"], 0), axis=[1, 2, 3]
              )
  
          for field in fields:
              if field in target:
                  target[field] = target[field][keep]
  
      features["label"] = target
      return features
  
imports: from typing import Any, Dict
  import tensorflow
label_desc: Use image node_id 8 for calling a global function with example usage: crop(features, region) and returns: features