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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def generate_clk_from_csv(input_f, # type: TextIO keys, # type: Tuple[AnyStr, AnyStr] schema, # type: Schema validate=True, # type: bool header=True, # type: Union[bool, AnyStr] progress_bar=True # type: bool ):
 """ Generate Bloom filters from CSV file, then serialise them. This function also computes and outputs the Hamming weight (a.k.a popcount -- the number of bits set to high) of the generated Bloom filters. :param input_f: A file-like object of csv data to hash. :param keys: A tuple of two lists of secret keys. :param schema: Schema specifying the record formats and hashing settings. :param validate: Set to `False` to disable validation of data against the schema. Note that this will silence warnings whose aim is to keep the hashes consistent between data sources; this may affect linkage accuracy. :param header: Set to `False` if the CSV file does not have a header. Set to `'ignore'` if the CSV file does have a header but it should not be checked against the schema. :param bool progress_bar: Set to `False` to disable the progress bar. :return: A list of serialized Bloom filters and a list of corresponding popcounts. """

    if header not in {False, True, 'ignore'}:
        raise ValueError("header must be False, True or 'ignore' but is {}."
                         .format(header))

    log.info("Hashing data")

    # Read from CSV file
    reader = unicode_reader(input_f)

    if header:
        column_names = next(reader)
        if header != 'ignore':
            validate_header(schema.fields, column_names)

    start_time = time.time()

    # Read the lines in CSV file and add it to PII
    pii_data = []
    for line in reader:
        pii_data.append(tuple(element.strip() for element in line))

    validate_row_lengths(schema.fields, pii_data)

    if progress_bar:
        stats = OnlineMeanVariance()
        with tqdm(desc="generating CLKs", total=len(pii_data), unit='clk', unit_scale=True,
                  postfix={'mean': stats.mean(), 'std': stats.std()}) as pbar:
            def callback(tics, clk_stats):
                stats.update(clk_stats)
                pbar.set_postfix(mean=stats.mean(), std=stats.std(), refresh=False)
                pbar.update(tics)

            results = generate_clks(pii_data,
                                    schema,
                                    keys,
                                    validate=validate,
                                    callback=callback)
    else:
        results = generate_clks(pii_data,
                                schema,
                                keys,
                                validate=validate)

    log.info("Hashing took {:.2f} seconds".format(time.time() - start_time))
    return results