def forward(self, inputs):
    feature_emb = self.embedding_layer(inputs)
    conv_in = torch.unsqueeze(feature_emb, 1)
    conv_out = self.conv_layer(conv_in)
    flatten_out = torch.flatten(conv_out, start_dim=1)
    y_pred = self.fc(flatten_out)
    if (self.output_activation is not None):
        y_pred = self.output_activation(y_pred)
    return y_pred
def forward(self, inputs):
    feature_emb = self.embedding_layer(inputs)
    conv_in = torch.unsqueeze(feature_emb, 1)
    conv_out = self.conv_layer(conv_in)
    flatten_out = torch.flatten(conv_out, start_dim=1)
    y_pred = self.fc(flatten_out)
    if (self.output_activation is not None):
        y_pred = self.output_activation(y_pred)
    return y_pred