Sorry for late response, had gone out for a while.
You are again confusing the nn.Linear layer. Suppose the following:
out = self.DropoutLayer(out)
# Out here gives you the shape [8, 32, 19, 11]

The significance of the numbers :- 8 is the batch size, 32 is the number of channels and 19, 11 are the height and width of the image.
Now you want to add a Linear layer which will simply take the flattened version of the input i.e. (8, 32*19*11) as input.
Now the point you are missing. When you use a dense layer you want to give the output size as well. Like if you have 10 classes as output, you want to add 10 neurons in the layer.
in_features -> input size (in your case it is 32*19*11)
out_features -> output size (which you have not defined, I think it is 128)
# In the __init__
self.FullyConnected = nn.Linear(32*19*11, 128)

# In the forward
out = out.view(-1, 32*19*11)
# As linear layer takes a 2D input where the first dim is batch size
out = self.FullyConnected(out)