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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 language_model_learner(data:DataBunch, arch, config:dict=None, drop_mult:float=1., pretrained:bool=True,
                           pretrained_fnames:OptStrTuple=None, **learn_kwargs) -> 'LanguageLearner':
    "Create a `Learner` with a language model from `data` and `arch`."
    model = get_language_model(arch, len(data.vocab.itos), config=config, drop_mult=drop_mult)
    meta = _model_meta[arch]
    learn = LanguageLearner(data, model, split_func=meta['split_lm'], **learn_kwargs)
    if pretrained:
        if 'url' not in meta: 
            warn("There are no pretrained weights for that architecture yet!")
            return learn
        model_path = untar_data(meta['url'], data=False)
        fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
        learn.load_pretrained(*fnames)
        learn.freeze()
    if pretrained_fnames is not None:
        fnames = [learn.path/learn.model_dir/f'{fn}.{ext}' for fn,ext in zip(pretrained_fnames, ['pth', 'pkl'])]
        learn.load_pretrained(*fnames)
        learn.freeze()
    return learn