def _shear_bbox(bbox, image_height, image_width, level, shear_horizontal):
  """Shifts the bbox according to how the image was sheared.

  Args:
    bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)
      of type float that represents the normalized coordinates between 0 and 1.
    image_height: Int, height of the image.
    image_width: Int, height of the image.
    level: Float. How much to shear the image.
    shear_horizontal: If true then shear in X dimension else shear in
      the Y dimension.

  Returns:
    A tensor of the same shape as bbox, but now with the shifted coordinates.
  """
  image_height, image_width = (
      tf.to_float(image_height), tf.to_float(image_width))

  # Change bbox coordinates to be pixels.
  min_y = tf.to_int32(image_height * bbox[0])
  min_x = tf.to_int32(image_width * bbox[1])
  max_y = tf.to_int32(image_height * bbox[2])
  max_x = tf.to_int32(image_width * bbox[3])
  coordinates = tf.stack(
      [[min_y, min_x], [min_y, max_x], [max_y, min_x], [max_y, max_x]])
  coordinates = tf.cast(coordinates, tf.float32)

  # Shear the coordinates according to the translation matrix.
  if shear_horizontal:
    translation_matrix = tf.stack(
        [[1, 0], [-level, 1]])
  else:
    translation_matrix = tf.stack(
        [[1, -level], [0, 1]])
  translation_matrix = tf.cast(translation_matrix, tf.float32)
  new_coords = tf.cast(
      tf.matmul(translation_matrix, tf.transpose(coordinates)), tf.int32)

  # Find min/max values and convert them back to floats.
  min_y = tf.to_float(tf.reduce_min(new_coords[0, :])) / image_height
  min_x = tf.to_float(tf.reduce_min(new_coords[1, :])) / image_width
  max_y = tf.to_float(tf.reduce_max(new_coords[0, :])) / image_height
  max_x = tf.to_float(tf.reduce_max(new_coords[1, :])) / image_width

  # Clip the bboxes to be sure the fall between [0, 1].
  min_y, min_x, max_y, max_x = _clip_bbox(min_y, min_x, max_y, max_x)
  min_y, min_x, max_y, max_x = _check_bbox_area(min_y, min_x, max_y, max_x)
  return tf.stack([min_y, min_x, max_y, max_x])
Shifts the bbox according to how the image was sheared.

  Args:
    bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)
      of type float that represents the normalized coordinates between 0 and 1.
    image_height: Int, height of the image.
    image_width: Int, height of the image.
    level: Float. How much to shear the image.
    shear_horizontal: If true then shear in X dimension else shear in
      the Y dimension.

  Returns:
    A tensor of the same shape as bbox, but now with the shifted coordinates.
  
inference_api/src/main/object_detection/utils/autoaugment_utils.py