Hi,
If I understood correctly, you mean something like Unet or resnet by skip-connections, right? If yes, the answer is yes too! I have not used Netron but when I tried visualizing model (Unet: skip connection between encoder and decoder layers) using Tensorboard or TensorboardX, it was ok and I could see the connections.
Here is the link to my implementation on GitHub 5
But the main part of code that do the cat thing is here:
class Expand(nn.Module):
    def __init__(self, input_channel, output_channel, ks=4, s=2):
        """
        This path consists of an up sampling of the feature map followed by a
        4x4 convolution ("up-convolution" or Transformed Convolution) that halves the number of
        feature channels, a concatenation with the correspondingly cropped feature map from Contract phase
        :param input_channel: input channel size
        :param output_channel: output channel size
        """
        super(Expand, self).__init__()
        self.layers = CE(input_channel * 2, output_channel, ks, s)

    def forward(self, x1, x2):
        delta_x = x1.size()[2] - x2.size()[2]
        delta_y = x1.size()[3] - x2.size()[3]
        x2 = F.pad(x2, pad=(delta_x // 2, delta_y // 2, delta_x // 2, delta_y // 2), mode='constant', value=0)
        x = torch.cat((x2, x1), dim=1)
        x = self.layers(x)
        return x

And for visualizing I did not specified any particular arg, etc and you do not need too, because the graph of the model constructed by the flow of data through network, so if you have a skip-connection, the data would flow that way too.