How would you like to combine the outputs from A and B in the first approach?
If you would like to concatenate both activations in the channel dimension, you would have to make sure that the spatial and batch dimensions match.
You could use a pooling operation on the output of A, such that its spatial shape equals the output of B.
Afterwards you could use x = torch.cat((out_a, out_b), dim=1) to create the input for C.
For the second approach you would have to make sure InceptionB takes an input of [batch_size, 256, 28, 28].
The inception model from torchvision accepts an input in the shape [batch_size, 3, 229, 229], so you might need to adapt the architecture of your model.