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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 init_model( lang, output_dir, freqs_loc=None, clusters_loc=None, jsonl_loc=None, vectors_loc=None, prune_vectors=-1, ):
 """ Create a new model from raw data, like word frequencies, Brown clusters and word vectors. If vectors are provided in Word2Vec format, they can be either a .txt or zipped as a .zip or .tar.gz. """

    if jsonl_loc is not None:
        if freqs_loc is not None or clusters_loc is not None:
            settings = ["-j"]
            if freqs_loc:
                settings.append("-f")
            if clusters_loc:
                settings.append("-c")
            msg.warn(
                "Incompatible arguments",
                "The -f and -c arguments are deprecated, and not compatible "
                "with the -j argument, which should specify the same "
                "information. Either merge the frequencies and clusters data "
                "into the JSONL-formatted file (recommended), or use only the "
                "-f and -c files, without the other lexical attributes.",
            )
        jsonl_loc = ensure_path(jsonl_loc)
        lex_attrs = srsly.read_jsonl(jsonl_loc)
    else:
        clusters_loc = ensure_path(clusters_loc)
        freqs_loc = ensure_path(freqs_loc)
        if freqs_loc is not None and not freqs_loc.exists():
            msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
        lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc)

    with msg.loading("Creating model..."):
        nlp = create_model(lang, lex_attrs)
    msg.good("Successfully created model")
    if vectors_loc is not None:
        add_vectors(nlp, vectors_loc, prune_vectors)
    vec_added = len(nlp.vocab.vectors)
    lex_added = len(nlp.vocab)
    msg.good(
        "Sucessfully compiled vocab",
        "{} entries, {} vectors".format(lex_added, vec_added),
    )
    if not output_dir.exists():
        output_dir.mkdir()
    nlp.to_disk(output_dir)
    return nlp