def forward(self, x):
    new_features = super(_DenseLayer, self).forward(x)
    if (self.drop_rate > 0):
        new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
    return torch.cat([x, new_features], 1)
def forward(self, x):
    new_features = super(_DenseLayer, self).forward(x)
    if (self.drop_rate > 0):
        new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
    return torch.cat([x, new_features], 1)