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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 read_vcf(input, fields=None, exclude_fields=None, rename_fields=None, types=None, numbers=None, alt_number=DEFAULT_ALT_NUMBER, fills=None, region=None, tabix='tabix', samples=None, transformers=None, buffer_size=DEFAULT_BUFFER_SIZE, chunk_length=DEFAULT_CHUNK_LENGTH, log=None):
 """Read data from a VCF file into NumPy arrays. .. versionchanged:: 1.12.0 Now returns None if no variants are found in the VCF file or matching the requested region. Parameters input : string or file-like {input} fields : list of strings, optional {fields} exclude_fields : list of strings, optional {exclude_fields} rename_fields : dict[str -> str], optional {rename_fields} types : dict, optional {types} numbers : dict, optional {numbers} alt_number : int, optional {alt_number} fills : dict, optional {fills} region : string, optional {region} tabix : string, optional {tabix} samples : list of strings {samples} transformers : list of transformer objects, optional {transformers} buffer_size : int, optional {buffer_size} chunk_length : int, optional {chunk_length} log : file-like, optional {log} Returns ------- data : dict[str, ndarray] A dictionary holding arrays, or None if no variants were found. """


    # samples requested?
    # noinspection PyTypeChecker
    store_samples, fields = _prep_fields_param(fields)

    # setup
    fields, samples, headers, it = iter_vcf_chunks(
        input=input, fields=fields, exclude_fields=exclude_fields, types=types,
        numbers=numbers, alt_number=alt_number, buffer_size=buffer_size,
        chunk_length=chunk_length, fills=fills, region=region, tabix=tabix,
        samples=samples, transformers=transformers
    )

    # handle field renaming
    if rename_fields:
        rename_fields, it = _do_rename(it, fields=fields,
                                       rename_fields=rename_fields,
                                       headers=headers)

    # setup progress logging
    if log is not None:
        it = _chunk_iter_progress(it, log, prefix='[read_vcf]')

    # read all chunks into a list
    chunks = [d[0] for d in it]

    if chunks:

        # setup output
        output = dict()

        if len(samples) > 0 and store_samples:
            output['samples'] = samples

        # find array keys
        keys = sorted(chunks[0].keys())

        # concatenate chunks
        for k in keys:
            output[k] = np.concatenate([chunk[k] for chunk in chunks], axis=0)

    else:

        output = None

    return output