code: def _yield_mammography_with_abnormalities():
      """Generate a fake mammography image containing a set of abnormalities."""
      mammography = np.zeros(
          (MAMMOGRAPHY_HEIGHT, MAMMOGRAPHY_WIDTH), dtype=np.uint8
      )
      # Draw a rectangle representing the breast region.
      breast_h = np.random.randint(
          int(MAMMOGRAPHY_HEIGHT * 0.7),
          int(MAMMOGRAPHY_HEIGHT * 0.9) + 1,
      )
      breast_w = np.random.randint(
          int(MAMMOGRAPHY_WIDTH * 0.7), int(MAMMOGRAPHY_WIDTH * 0.9) + 1
      )
      breast_y = np.random.randint(0, MAMMOGRAPHY_HEIGHT - breast_h)
      breast_x = np.random.randint(0, MAMMOGRAPHY_WIDTH - breast_w)
      breast_intensity = np.random.randint(
          BREAST_INTENSITY_MIN, BREAST_INTENSITY_MAX + 1
      )
      mammography[
          breast_y : (breast_y + breast_h),
          breast_x : (breast_x + breast_w),
      ] = breast_intensity
  
      abnormalities = []  # Note: pairs of (mask, crop).
      for _ in range(np.random.randint(1, NUM_ABNORMALITIES_MAX + 1)):
          abnorm_h = np.random.randint(
              ABNORMALITY_SIZE_MIN, ABNORMALITY_SIZE_MAX + 1
          )
          abnorm_w = np.random.randint(
              ABNORMALITY_SIZE_MIN, ABNORMALITY_SIZE_MAX + 1
          )
          abnorm_y = (
              np.random.randint(0, breast_h - abnorm_h) + breast_y
          )
          abnorm_x = (
              np.random.randint(0, breast_w - abnorm_w) + breast_x
          )
          abnorm_intensity = np.random.randint(
              int(BREAST_INTENSITY_MIN * 1.2), 256
          )
          while np.absolute(abnorm_intensity - breast_intensity) < 10:
              abnorm_intensity = np.random.randint(
                  int(BREAST_INTENSITY_MIN * 1.2), 256
              )
          # Draw abnormality in the mammography.
          mammography[
              abnorm_y : (abnorm_y + abnorm_h),
              abnorm_x : (abnorm_x + abnorm_w),
          ] = abnorm_intensity
          # Abnormality mask w.r.t the full mammography.
          abnorm_mask = np.zeros_like(mammography)
          abnorm_mask[
              abnorm_y : (abnorm_y + abnorm_h),
              abnorm_x : (abnorm_x + abnorm_w),
          ] = 255
          # Abnormality crop.
          abnorm_crop = np.ones((abnorm_h, abnorm_w)) * abnorm_intensity
          abnormalities.append((abnorm_mask, abnorm_crop))
  
      return mammography, abnormalities
  
file_path: datasets/tensorflow_datasets/testing/fake_data_generation/curated_breast_imaging_ddsm.py
imports: import csv
  import os
  from absl import app
  from absl import flags
  import numpy
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling a global function with example usage: _yield_mammography_with_abnormalities() and returns: mammography,  abnormalities