Oh, with your help I solved problem finally.
model_headed = myVgg16Headed()
model_headed = nn.DataParallel(model_headed)
model_headed.load_state_dict(torch.load('./weight/vgg16_baseline.wts'))

In this cell, model_headed = nn.DataParallel(model_headed) solved the error.
And I have one more question about your code.
class myVgg16Headed(myVgg16):
    def __init__(self, num_classes=10):
        super(myVgg16Headed, self).__init__(num_classes)

    def forward(self, x_up, x_down):
        x_up = self.head(x_up)
        x_down = self.head(x_down)
        x = torch.cat((x_up, x_down), dim=2)
        
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

Above code means that class myVgg16Headed(myVgg16) overridemyVgg16’s def forward ??