code: def adjust_bboxes(
      bboxes, old_height, old_width, new_height, new_width
  ):
      """Adjusts the bboxes of an image that has been resized.
  
      Args:
          bboxes: Tensor with shape (num_bboxes, 4).
          old_height: Float. Height of the original image.
          old_width: Float. Width of the original image.
          new_height: Float. Height of the image after resizing.
          new_width: Float. Width of the image after resizing.
      Returns:
          Tensor with shape (num_bboxes, 4), with the adjusted bboxes.
      """
      # x_min, y_min, x_max, y_max = np.split(bboxes, 4, axis=1)
      x_min = bboxes[:, 0] / old_width
      y_min = bboxes[:, 1] / old_height
      x_max = bboxes[:, 2] / old_width
      y_max = bboxes[:, 3] / old_height
  
      # Use new size to scale back the bboxes points to absolute values.
      x_min = x_min * new_width
      y_min = y_min * new_height
      x_max = x_max * new_width
      y_max = y_max * new_height
  
      # Concat points and label to return a [num_bboxes, 4] tensor.
      return np.stack([x_min, y_min, x_max, y_max], axis=1)
  
imports: import numpy
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: adjust_bboxes(bboxes, old_height, old_width, new_height, new_width) and returns: np