code: def _generate_data_calc(output_dir, number_of_mammograms):
      """Generate train/test CSV and images of calcification abnormalities."""
      calc_types = tfds.features.ClassLabel(
          names_file=tfds.core.tfds_path(
              os.path.join("image", "cbis_ddsm_calc_types.txt")
          )
      ).names
      calc_distributions = tfds.features.ClassLabel(
          names_file=tfds.core.tfds_path(
              os.path.join("image", "cbis_ddsm_calc_distributions.txt")
          )
      ).names
      _generate_csv(
          os.path.join(
              output_dir, "calc_case_description_train_set.csv"
          ),
          lambda: _yield_csv_rows_calc(
              output_dir, calc_types, calc_distributions
          ),
          number_of_mammograms[0],
      )
      _generate_csv(
          os.path.join(
              output_dir, "calc_case_description_test_set.csv"
          ),
          lambda: _yield_csv_rows_calc(
              output_dir, calc_types, calc_distributions
          ),
          number_of_mammograms[1],
      )
  
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 8 for calling a global function with example usage: _generate_data_calc(output_dir, number_of_mammograms) without return types