How to use the dnn.pytorch.layer.softmax_layer function in dnn

self.n_hierarchy = n_hierarchy
        self.mode = mode
        self.bi_direction_num = 2 if self.bi_direction else 1

        self.emb_mat = layer.embedding_layer(emb_matrix, self.emb_type)
        self.drop_out = nn.Dropout(self.drop_prob)

        rnn_params = (self.n_hidden, self.n_layer, self.drop_prob, self.bi_direction, self.rnn_type)
        self.rnn = nn.ModuleList([layer.RNN_layer(self.emb_dim, *rnn_params)])
        self.att = nn.ModuleList([layer.self_attention_layer(self.bi_direction_num * self.n_hidden)])
        for _ in range(self.n_hierarchy - 1):
            self.rnn.append(layer.RNN_layer(self.bi_direction_num * self.n_hidden, *rnn_params))
            if self.use_attention:
                self.att.append(layer.self_attention_layer(self.bi_direction_num * self.n_hidden))
        self.predict = layer.softmax_layer(self.n_hidden * self.bi_direction_num, self.n_class)

def __init__(self, emb_matrix, args):
        super(LSTM_model, self).__init__()

        # Embedding layer
        self.emb_mat = layer.embedding_layer(emb_mat, 'const')
        # Drop out layer
        self.drop_out = nn.Dropout(args.drop_prob)
        # LSTM layer
        self.lstm = layer.RNN_layer(args.emb_dim, args.n_hidden, args.n_layer,
                                    args.drop_prob, args.bi_direction, mode="LSTM")
        # SoftMax layer
        bi_direction_num = 2 if args.bi_direction else 1
        self.predictor = layer.softmax_layer(bi_direction_num * args.n_hidden, args.n_class)

self.n_time = n_time
        self.bi_direction_num = 2 if self.bi_direction else 1
        out_n_hidden = self.n_hidden * self.bi_direction_num
        self.drop_out = nn.Dropout(self.drop_prob)
        self.embedding_layer(emb_matrix)

        self.extractors = nn.ModuleList()
        self.attentions = nn.ModuleList()
        self.predictors = nn.ModuleList()
        for _ in range(n_time):
            self.extractors.append(
                nn.ModuleList([layer.CNN_layer(self.emb_dim, 1, self.n_hidden, kw) for kw in range(1, 3)])
            )  # index 0 -> (nt-1)
            self.attentions.append(layer.self_attention_layer(out_n_hidden))
            self.predictors.append(layer.softmax_layer(out_n_hidden, self.n_class))  # index 0 -> (nt-1)
        self.connections = nn.ModuleList()
        self.connections.append(None)
        for _ in range(n_time - 1):
            self.connections.append(
                nn.Sequential(
                    nn.Linear(2 * out_n_hidden, out_n_hidden, bias=False),
                    nn.Sigmoid()
                )

def __init__(self, emb_matrix, args, kernel_widths):
        """
        Initilize the model data and layer
        * emb_matrix [np.array]: word embedding matrix
        * args [dict]: all model arguments
        * kernel_widths [list]: list of kernel widths for cnn kernel
        """
        nn.Module.__init__(self)
        base.base.__init__(self, args)

        self.emb_mat = layer.embedding_layer(emb_matrix, self.emb_type)
        self.drop_out = nn.Dropout(self.drop_prob)
        self.cnn = nn.ModuleList()
        for kw in kernel_widths:
            self.cnn.append(layer.CNN_layer(self.emb_dim, 1, self.n_hidden, kw))
        self.predict = layer.softmax_layer(self.n_hidden * len(kernel_widths), self.n_class)

def __init__(self, emb_matrix, args):
        """
        Initilize the model data and layer
        * emb_matrix [np.array]: word embedding matrix
        * args [dict]: all model arguments
        """
        nn.Module.__init__(self)
        base.base.__init__(self, args)

        self.emb_mat = layer.embedding_layer(emb_matrix, self.emb_type)
        self.pos_emb_mat = layer.positional_embedding_layer(self.n_hidden)
        self.drop_out = nn.Dropout(self.drop_prob)
        self.transformer = nn.ModuleList([
            layer.transformer_layer(self.emb_dim, self.n_hidden, self.n_head) for _ in range(self.n_layer)
        ])
        self.predict = layer.softmax_layer(self.emb_dim, self.n_class)