code: def tf_crop_and_flip(image_buffer, num_channels=3):
      """Crops the given image to a random part of the image, and randomly flips.
  
      We use the fused decode_and_crop op, which performs better than the two ops
      used separately in series, but note that this requires that the image be
      passed in as an un-decoded string Tensor.
  
      Args:
          image_buffer: scalar string Tensor representing the raw JPEG image buffer.
          bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
              where each coordinate is [0, 1) and the coordinates are arranged as
              [ymin, xmin, ymax, xmax].
          num_channels: Integer depth of the image buffer for decoding.
  
      Returns:
          3-D tensor with cropped image.
  
      """
      # A large fraction of image datasets contain a human-annotated bounding box
      # delineating the region of the image containing the object of interest.    We
      # choose to create a new bounding box for the object which is a randomly
      # distorted version of the human-annotated bounding box that obeys an
      # allowed range of aspect ratios, sizes and overlap with the human-annotated
      # bounding box. If no box is supplied, then we assume the bounding box is
      # the entire image.
      shape = tf.shape(image_buffer)
      if len(shape) == num_channels + 1:
          shape = shape[1:]
  
      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(
              shape,
              bounding_boxes=bbox,
              min_object_covered=0.1,
              aspect_ratio_range=[0.75, 1.33],
              area_range=[0.05, 1.0],
              max_attempts=100,
              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)
  
      image_buffer = tf.image.crop_to_bounding_box(
          image_buffer,
          offset_height=offset_y,
          offset_width=offset_x,
          target_height=target_height,
          target_width=target_width,
      )
      # Flip to add a little more random distortion in.
      image_buffer = tf.image.random_flip_left_right(image_buffer)
      image_buffer = tf.compat.v1.image.resize(
          image_buffer,
          [DEFAULT_IMAGE_SIZE, DEFAULT_IMAGE_SIZE],
          method=tf.image.ResizeMethod.BILINEAR,
          align_corners=False,
      )
      return image_buffer
  
imports: import ray
  import torch
  import torchvision
  from torchvision.transforms.functional import pil_to_tensor
  import os
  import tensorflow
  import numpy
  from PIL import Image
  from typing import TYPE_CHECKING, Iterator, Callable, Any
  import pandas
  from benchmark import Benchmark
  import streaming
  from streaming import LocalDataset, StreamingDataset
  from ray.data._internal.delegating_block_builder import DelegatingBlockBuilder
label_desc: Use image node_id 3 for calling a global function with example usage: tf_crop_and_flip(image_buffer, num_channels) and returns: image_buffer