First, you have to change the ’out_channels of your conv layer in order to stay as close as possible to your keras model. E.g. conv1 needs to have out_channels=2.
As a next step, your dense layers need to be implemented in another way. The first dense layer e.g. has 96 parameters which means
        self.dense1 = nn.Linear(2, 32)

The dense layer operates in the last dimension as far as I know, so in my opinion you would need something like
 x = x.view(1, -1, 2)
 x = self.dense1(x)
 x = x.view(1, 32, 25, 67)

with batch_size = 1. Or transposing would also be an option. But to be honest, I dont really know if this is the best solution or implementation, I just wanted to raise awareness for the differences between the given model and yours.
Best regards