Create a text classifier from `arch` and its `config`, maybe `pretrained`.

def get_text_classifier(arch:Callable, vocab_sz:int, n_class:int, bptt:int=70, max_len:int=20*70, config:dict=None, 
                        drop_mult:float=1., lin_ftrs:Collection[int]=None, ps:Collection[float]=None,
                        pad_idx:int=1) -> nn.Module:
    "Create a text classifier from `arch` and its `config`, maybe `pretrained`."
    meta = _model_meta[arch]
    config = ifnone(config, meta['config_clas'].copy())
    for k in config.keys(): 
        if k.endswith('_p'): config[k] *= drop_mult
    if lin_ftrs is None: lin_ftrs = [50]
    if ps is None:  ps = [0.1]*len(lin_ftrs)
    layers = [config[meta['hid_name']] * 3] + lin_ftrs + [n_class]
    ps = [config.pop('output_p')] + ps
    init = config.pop('init') if 'init' in config else None
    encoder = MultiBatchEncoder(bptt, max_len, arch(vocab_sz, **config), pad_idx=pad_idx)
    model = SequentialRNN(encoder, PoolingLinearClassifier(layers, ps))
    return model if init is None else model.apply(init)