If your features have a shape of [batch_size, 2208, 7, 7], the general approach would be to flatten this tensor to [batch_size, 2208*7*7] or to use some kind of pooling to get a tensor of [batch_size, 2208, 1, 1] and just squeeze dim2 and dim3 before passing it to the linear layer.
In the former case, you would have to define in_features=2208*7*7 in your linear layer.