Yes, with sum you are right: I take vgg_a and vgg_b and I sum layer per layer their weights.
For showing the parameters, I mean this, I am not doing in a right way the print of the weights.
I have these:
model1 = VGG16((1,32,32),batch_norm=True)
model2 = VGG16((1,32,32),batch_norm=True)
model3 = VGG16((1,32,32),batch_norm=True)
classifier = Classifier(num_classes=1)

nets = [model1,model2,model3,classifier]

where VGG16 and Classifier are:
class VGGBlock(nn.Module):
    def __init__(self, in_channels, out_channels,batch_norm=False):

        super().__init__()

        conv2_params = {'kernel_size': (3, 3),
                        'stride'     : (1, 1),
                        'padding'   : 1
                        }

        noop = lambda x : x

        self._batch_norm = batch_norm

        self.conv1 = nn.Conv2d(in_channels=in_channels,out_channels=out_channels , **conv2_params)
        self.bn1 = nn.BatchNorm2d(out_channels) if batch_norm else noop

        self.conv2 = nn.Conv2d(in_channels=out_channels,out_channels=out_channels, **conv2_params)
        self.bn2 = nn.BatchNorm2d(out_channels) if batch_norm else noop

        self.max_pooling = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))

    @property
    def batch_norm(self):
        return self._batch_norm

    def forward(self,x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = F.relu(x)

        x = self.max_pooling(x)

        return x

class VGG16(nn.Module):

  def __init__(self, input_size, batch_norm=False):
    super(VGG16, self).__init__()

    self.in_channels,self.in_width,self.in_height = input_size

    self.block_1 = VGGBlock(self.in_channels,64,batch_norm=batch_norm)
    self.block_2 = VGGBlock(64, 128,batch_norm=batch_norm)
    self.block_3 = VGGBlock(128, 256,batch_norm=batch_norm)
    self.block_4 = VGGBlock(256,512,batch_norm=batch_norm)


  @property
  def input_size(self):
      return self.in_channels,self.in_width,self.in_height

  def forward(self, x):

    x = self.block_1(x)
    x = self.block_2(x)
    x = self.block_3(x)
    x = self.block_4(x)
    # x = self.avgpool(x)
    x = torch.flatten(x,1)

    return x

class Classifier(nn.Module):

    def __init__(self,num_classes=1):
        super().__init__()

        self.classifier = nn.Sequential(
            nn.Linear(2048, 2048),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(2048, 512),
            nn.ReLU(True),
            nn.Dropout(p=0.5),
            nn.Linear(512, num_classes)
        )

    def forward(self,x):

        return self.classifier(x)


And in the train there is this part to do the sum:
for n in model_a:
              layer_a = model_a[n]
              layer_b = model_b[n]
              layer_c = model_c[n]
              if (isinstance(layer_a,nn.Conv2d)):
                loss += lambda_reg * reg_loss(combo_fn(layer_a.weight,layer_b.weight),layer_c.weight)
                if (layer_a.bias is not None):
                  loss += lambda_reg * reg_loss(combo_fn(layer_a.bias, layer_b.bias), layer_c.bias)