code: def _btgraham_processing(
      image_fobj, filepath, target_pixels, crop_to_radius=False
  ):
      """Process an image as the winner of the 2015 Kaggle competition.
  
      Args:
        image_fobj: File object containing the original image.
        filepath: Filepath of the image, for logging purposes only.
        target_pixels: The number of target pixels for the radius of the image.
        crop_to_radius: If True, crop the borders of the image to remove gray areas.
  
      Returns:
        A file object.
      """
      cv2 = tfds.core.lazy_imports.cv2
      # Decode image using OpenCV2.
      image = cv2.imdecode(
          np.frombuffer(image_fobj.read(), dtype=np.uint8), flags=3
      )
      # Process the image.
      image = _scale_radius_size(
          image, filepath, target_radius_size=target_pixels
      )
      image = _subtract_local_average(
          image, target_radius_size=target_pixels
      )
      image = _mask_and_crop_to_radius(
          image,
          target_radius_size=target_pixels,
          radius_mask_ratio=0.9,
          crop_to_radius=crop_to_radius,
      )
      # Encode the image with quality=72 and store it in a BytesIO object.
      _, buff = cv2.imencode(
          ".jpg", image, [int(cv2.IMWRITE_JPEG_QUALITY), 72]
      )
      return io.BytesIO(buff.tobytes())
  
imports: import csv
  import io
  import os
  from absl import logging
  from etils import epath
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling a global function with example usage: _btgraham_processing(image_fobj, filepath, target_pixels, crop_to_radius) and returns: io