code: def _yield_csv_rows_calc(output_dir, calc_types, calc_distributions):
      """Generate a row for the calcification abnormalities."""
  
      def _row_extra_info_gen_fn():
          return {
              "calc type": np.random.choice(calc_types),
              "calc distribution": np.random.choice(calc_distributions),
          }
  
      return _yield_csv_rows_base(output_dir, _row_extra_info_gen_fn)
  
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 5 for calling a global function with example usage: _yield_csv_rows_calc(output_dir, calc_types, calc_distributions) and returns: _yield_csv_rows_base, dict