code: def _decode_crop_and_flip(image_buffer, num_channels):
      min_object_covered = 0.1
      aspect_ratio_range = [0.75, 1.33]
      area_range = [0.05, 1.0]
      max_attempts = 100
  
      bbox = tf.constant(
          [0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]
      )  # From the entire image
      sample_distorted_bounding_box = (
          tf.image.sample_distorted_bounding_box(
              image_size=tf.image.extract_jpeg_shape(image_buffer),
              bounding_boxes=bbox,
              min_object_covered=min_object_covered,
              aspect_ratio_range=aspect_ratio_range,
              area_range=area_range,
              max_attempts=max_attempts,
              use_image_if_no_bounding_boxes=True,
          )
      )
      bbox_begin, bbox_size, _ = sample_distorted_bounding_box
  
      # Reassemble the bounding box in the format the crop op requires.
      offset_y, offset_x, _ = tf.unstack(bbox_begin)
      target_height, target_width, _ = tf.unstack(bbox_size)
      crop_window = tf.stack(
          [offset_y, offset_x, target_height, target_width]
      )
  
      # Use the fused decode and crop op here, which is faster than each in series.
      cropped = tf.image.decode_and_crop_jpeg(
          image_buffer, crop_window, channels=num_channels
      )
  
      # Flip to add a little more random distortion in.
      cropped = tf.image.random_flip_left_right(cropped)
      return cropped
  
imports: import os
  import argparse
  import tensorflow
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca import init_orca_context, stop_orca_context
label_desc: Use image node_id 1 for calling a global function with example usage: _decode_crop_and_flip(image_buffer, num_channels) and returns: cropped