code: def _crop(
      image, offset_height, offset_width, crop_height, crop_width
  ):
      """Crops the given image using the provided offsets and sizes.
  
      Note that the method doesn't assume we know the input image size but it does
      assume we know the input image rank.
  
      Args:
        image: an image of shape [height, width, channels].
        offset_height: a scalar tensor indicating the height offset.
        offset_width: a scalar tensor indicating the width offset.
        crop_height: the height of the cropped image.
        crop_width: the width of the cropped image.
  
      Returns:
        the cropped (and resized) image.
  
      Raises:
        InvalidArgumentError: if the rank is not 3 or if the image dimensions are
          less than the crop size.
      """
      original_shape = tf.shape(image)
  
      rank_assertion = tf.Assert(
          tf.equal(tf.rank(image), 3),
          ["Rank of image must be equal to 3."],
      )
      with tf.control_dependencies([rank_assertion]):
          cropped_shape = tf.stack(
              [crop_height, crop_width, original_shape[2]]
          )
  
      size_assertion = tf.Assert(
          tf.logical_and(
              tf.greater_equal(original_shape[0], crop_height),
              tf.greater_equal(original_shape[1], crop_width),
          ),
          ["Crop size greater than the image size."],
      )
  
      offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))
  
      # Use tf.strided_slice instead of crop_to_bounding box as it accepts tensors
      # to define the crop size.
      with tf.control_dependencies([size_assertion]):
          image = tf.strided_slice(
              image,
              offsets,
              offsets + cropped_shape,
              strides=tf.ones_like(offsets),
          )
      return tf.reshape(image, cropped_shape)
  
imports: import io
  import os
  import tempfile
  from magenta.models.image_stylization import imagenet_data
  import numpy
  import skimage.io
  import tensorflow.compat.v1
label_desc: Use image node_id 10 for calling a global function with example usage: _crop(image, offset_height, offset_width, crop_height, crop_width) and returns: tf