code: def padding_augmenter(
      image,
      annotation,
      random,
      skip_probability=_DEFAULT_AUG_PARAMS["skip_probability_pad"],
      min_aspect_ratio=_DEFAULT_AUG_PARAMS["min_aspect_ratio"],
      max_aspect_ratio=_DEFAULT_AUG_PARAMS["max_aspect_ratio"],
      min_area_fraction=_DEFAULT_AUG_PARAMS["min_area_fraction_pad"],
      max_area_fraction=_DEFAULT_AUG_PARAMS["max_area_fraction_pad"],
      max_attempts=_DEFAULT_AUG_PARAMS["max_attempts"],
  ):
      if random.uniform(0.0, 1.0) < skip_probability:
          return np.array(image), annotation
  
      image_height, image_width, _ = image.shape
  
      # Randomly sample aspect ratios until one derives a non-empty range of
      # compatible heights, or until reaching the upper limit on attempts.
      for i in range(max_attempts):
          # Randomly sample an aspect ratio.
          aspect_ratio = random.uniform(
              min_aspect_ratio, max_aspect_ratio
          )
  
          # The padded height must be at least as large as the original height.
          # h' >= h
          min_height = float(image_height)
  
          # The padded width must be at least as large as the original width.
          # w' >= w IMPLIES ah' >= w IMPLIES h' >= w / a
          min_height_from_width = float(image_width) / aspect_ratio
          if min_height < min_height_from_width:
              min_height = min_height_from_width
  
          # The padded area must attain the minimum area fraction.
          # w'h' >= fhw IMPLIES ah'h' >= fhw IMPLIES h' >= sqrt(fhw/a)
          min_height_from_area = np.sqrt(
              image_height
              * image_width
              * min_area_fraction
              / aspect_ratio
          )
          if min_height < min_height_from_area:
              min_height = min_height_from_area
  
          # The padded area must not exceed the maximum area fraction.
          max_height = np.sqrt(
              image_height
              * image_width
              * max_area_fraction
              / aspect_ratio
          )
  
          if min_height >= max_height:
              break
  
      # We did not find a compatible aspect ratio. Just return the original data.
      if min_height > max_height:
          return np.array(image), annotation
  
      # Sample a final size, given the sampled aspect ratio and range of heights.
      padded_height = random.uniform(min_height, max_height)
      padded_width = padded_height * aspect_ratio
  
      # Sample the offset of the source image inside the padded image.
      x_offset = random.uniform(0.0, (padded_width - image_width))
      y_offset = random.uniform(0.0, (padded_height - image_height))
  
      # Compute padding needed on the image
      after_padding_width = padded_width - image_width - x_offset
      after_padding_height = padded_height - image_height - y_offset
  
      # Pad the image
      npad = (
          (int(y_offset), int(after_padding_height)),
          (int(x_offset), int(after_padding_width)),
          (0, 0),
      )
      padded_image = np.pad(
          image, pad_width=npad, mode="constant", constant_values=0.5
      )
  
      ty = float(y_offset)
      tx = float(x_offset)
  
      # Transformation matrix for the annotations
      transformation_matrix = np.array(
          [[1.0, 0.0, ty], [0.0, 1.0, tx], [0.0, 0.0, 1.0]]
      )
  
      # Use transformation matrix to augment annotations
      formatted_annotation = []
      for aug in annotation:
          identifier = aug[0:1]
          bounds = aug[1:5]
          confidence = aug[5:6]
  
          if not np.any(bounds):
              formatted_annotation.append(
                  np.concatenate(
                      [identifier, np.array([0, 0, 0, 0]), confidence]
                  )
              )
              continue
  
          width = bounds[2]
          height = bounds[3]
  
          x1 = bounds[0] * image_width
          y1 = bounds[1] * image_height
          x2 = (bounds[0] + width) * image_width
          y2 = (bounds[1] + height) * image_height
  
          augmentation_coordinates = np.array(
              [y1, x1, y2, x2], dtype=np.float32
          )
  
          v = np.concatenate(
              [
                  augmentation_coordinates.reshape((2, 2)),
                  np.ones((2, 1), dtype=np.float32),
              ],
              axis=1,
          )
          transposed_v = np.dot(v, np.transpose(transformation_matrix))
          t_intersection = np.squeeze(
              transposed_v[:, :2].reshape(-1, 4)
          )
  
          # Sort the points top, left, bottom, right
          if t_intersection[0] > t_intersection[2]:
              t_intersection[0], t_intersection[2] = (
                  t_intersection[2],
                  t_intersection[0],
              )
          if t_intersection[1] > t_intersection[3]:
              t_intersection[1], t_intersection[3] = (
                  t_intersection[3],
                  t_intersection[1],
              )
  
          # Normalize the elements to the cropped width and height
          ele_1 = t_intersection[1] / padded_width
          ele_2 = t_intersection[0] / padded_height
          ele_3 = (t_intersection[3] - t_intersection[1]) / padded_width
          ele_4 = (
              t_intersection[2] - t_intersection[0]
          ) / padded_height
  
          formatted_annotation.append(
              np.concatenate(
                  [
                      identifier,
                      np.array([ele_1, ele_2, ele_3, ele_4]),
                      confidence,
                  ]
              )
          )
  
      return np.array(padded_image), np.array(
          formatted_annotation, dtype=np.float32
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import numpy
  from PIL import Image
  import turicreate.toolkits._tf_utils
  import turicreate
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 7 for calling a global function with example usage: padding_augmenter(image, annotation, random, skip_probability, min_aspect_ratio, max_aspect_ratio, min_area_fraction, max_area_fraction, max_attempts) and returns: np,  np, np,  annotation, np,  annotation