How to use the depccg.lstm_parser_bi_fast.Linear function in depccg

def __init__(self, model_path, word_dim=None, afix_dim=None, nlayers=2,
            hidden_dim=128, dep_dim=100, dropout_ratio=0.5):
        Param.load(self, os.path.join(model_path, 'tagger_defs.txt'))
        self.extractor = FeatureExtractor(model_path, length=True)
        self.in_dim = self.word_dim + 8 * self.afix_dim
        self.dropout_ratio = dropout_ratio
        super(FastBiaffineLSTMParser, self).__init__(
                emb_word=L.EmbedID(self.n_words, self.word_dim, ignore_label=IGNORE),
                emb_suf=L.EmbedID(self.n_suffixes, self.afix_dim, ignore_label=IGNORE),
                emb_prf=L.EmbedID(self.n_prefixes, self.afix_dim, ignore_label=IGNORE),
                lstm_f=FixedLengthNStepLSTM(self.nlayers, self.in_dim, self.hidden_dim, 0.32),
                lstm_b=FixedLengthNStepLSTM(self.nlayers, self.in_dim, self.hidden_dim, 0.32),
                arc_dep=Linear(2 * self.hidden_dim, self.dep_dim),
                arc_head=Linear(2 * self.hidden_dim, self.dep_dim),
                rel_dep=Linear(2 * self.hidden_dim, self.dep_dim),
                rel_head=Linear(2 * self.hidden_dim, self.dep_dim),
                biaffine_arc=Biaffine(self.dep_dim),
                biaffine_tag=Bilinear(self.dep_dim, self.dep_dim, len(self.targets)))

def __init__(self, model_path, word_dim=None, afix_dim=None, nlayers=2,
            hidden_dim=128, dep_dim=100, dropout_ratio=0.5):
        Param.load(self, os.path.join(model_path, 'tagger_defs.txt'))
        self.extractor = FeatureExtractor(model_path, length=True)
        self.in_dim = self.word_dim + 8 * self.afix_dim
        self.dropout_ratio = dropout_ratio
        super(FastBiaffineLSTMParser, self).__init__(
                emb_word=L.EmbedID(self.n_words, self.word_dim, ignore_label=IGNORE),
                emb_suf=L.EmbedID(self.n_suffixes, self.afix_dim, ignore_label=IGNORE),
                emb_prf=L.EmbedID(self.n_prefixes, self.afix_dim, ignore_label=IGNORE),
                lstm_f=FixedLengthNStepLSTM(self.nlayers, self.in_dim, self.hidden_dim, 0.32),
                lstm_b=FixedLengthNStepLSTM(self.nlayers, self.in_dim, self.hidden_dim, 0.32),
                arc_dep=Linear(2 * self.hidden_dim, self.dep_dim),
                arc_head=Linear(2 * self.hidden_dim, self.dep_dim),
                rel_dep=Linear(2 * self.hidden_dim, self.dep_dim),
                rel_head=Linear(2 * self.hidden_dim, self.dep_dim),
                biaffine_arc=Biaffine(self.dep_dim),
                biaffine_tag=Bilinear(self.dep_dim, self.dep_dim, len(self.targets)))

def __call__(self, x):
        shape = x.shape
        if len(shape) == 3:
            x = F.reshape(x, (-1, shape[2]))
        y = super(Linear, self).__call__(x)
        if len(shape) == 3:
            y = F.reshape(y, (shape[0], shape[1], -1))
        return y

def __init__(self, model_path, word_dim=None, afix_dim=None, nlayers=2,
            hidden_dim=128, dep_dim=100, dropout_ratio=0.5):
        Param.load(self, os.path.join(model_path, 'tagger_defs.txt'))
        self.extractor = FeatureExtractor(model_path, length=True)
        self.in_dim = self.word_dim + 8 * self.afix_dim
        self.dropout_ratio = dropout_ratio
        super(FastBiaffineLSTMParser, self).__init__(
                emb_word=L.EmbedID(self.n_words, self.word_dim, ignore_label=IGNORE),
                emb_suf=L.EmbedID(self.n_suffixes, self.afix_dim, ignore_label=IGNORE),
                emb_prf=L.EmbedID(self.n_prefixes, self.afix_dim, ignore_label=IGNORE),
                lstm_f=FixedLengthNStepLSTM(self.nlayers, self.in_dim, self.hidden_dim, 0.32),
                lstm_b=FixedLengthNStepLSTM(self.nlayers, self.in_dim, self.hidden_dim, 0.32),
                arc_dep=Linear(2 * self.hidden_dim, self.dep_dim),
                arc_head=Linear(2 * self.hidden_dim, self.dep_dim),
                rel_dep=Linear(2 * self.hidden_dim, self.dep_dim),
                rel_head=Linear(2 * self.hidden_dim, self.dep_dim),
                biaffine_arc=Biaffine(self.dep_dim),
                biaffine_tag=Bilinear(self.dep_dim, self.dep_dim, len(self.targets)))

def __init__(self, model_path, word_dim=None, afix_dim=None, nlayers=2,
            hidden_dim=128, dep_dim=100, dropout_ratio=0.5):
        Param.load(self, os.path.join(model_path, 'tagger_defs.txt'))
        self.extractor = FeatureExtractor(model_path, length=True)
        self.in_dim = self.word_dim + 8 * self.afix_dim
        self.dropout_ratio = dropout_ratio
        super(FastBiaffineLSTMParser, self).__init__(
                emb_word=L.EmbedID(self.n_words, self.word_dim, ignore_label=IGNORE),
                emb_suf=L.EmbedID(self.n_suffixes, self.afix_dim, ignore_label=IGNORE),
                emb_prf=L.EmbedID(self.n_prefixes, self.afix_dim, ignore_label=IGNORE),
                lstm_f=FixedLengthNStepLSTM(self.nlayers, self.in_dim, self.hidden_dim, 0.32),
                lstm_b=FixedLengthNStepLSTM(self.nlayers, self.in_dim, self.hidden_dim, 0.32),
                arc_dep=Linear(2 * self.hidden_dim, self.dep_dim),
                arc_head=Linear(2 * self.hidden_dim, self.dep_dim),
                rel_dep=Linear(2 * self.hidden_dim, self.dep_dim),
                rel_head=Linear(2 * self.hidden_dim, self.dep_dim),
                biaffine_arc=Biaffine(self.dep_dim),
                biaffine_tag=Bilinear(self.dep_dim, self.dep_dim, len(self.targets)))