Yes, your usage of the nn.Sequential container will change the model and thus break it.
You can print it and will get:
Sequential(
  (0): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
  (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
  (3): Linear(in_features=400, out_features=120, bias=True)
  (4): Linear(in_features=120, out_features=84, bias=True)
  (5): Linear(in_features=84, out_features=10, bias=True)
)

which is using the pooling layer only once and is also missing the flattening (via x = x.view(...)) and will thus raise a shape mismatch error.