def next_sample(subdir, dirs, files):  # pylint: disable=unused-argument
    """Return the filenames of the problem."""
    # More could be extracted (like the expected inputs/outputs
    # pairs, the problem difficulty, the names of the algorithmic techniques
    # needed)
    desc_file = os.path.join(subdir, _DESC_DIR_NAME, "description.txt")
    code_files = []
    # As the dataset is noisy, the program deduce the language from the file
    # content.
    code_pattern = os.path.join(subdir, pb_cst.code_dir_name, "*.txt")
    for f in tf.gfile.Glob(code_pattern):
      with tf.gfile.GFile(f, mode="r") as target_file:
        # Hack to filter C++/Java files. In theory some python comments could
        # make the file be considered as C++ but in practice the chance of
        # getting a false negative is low.
        content = target_file.read()
        if not any(p in content for p in pb_cst.filter_patterns):
          code_files.append(f)
    return CodingPbInfo(
        desc_file=desc_file,
        code_files=code_files
    )
Return the filenames of the problem.
tensor2tensor/data_generators/desc2code.py