You could draw the mask manually using this code:
x = torch.randn(64, 10, 2, 30)
N, C, _, L = x.size()

if torch.randint(0, 2, (1,)) == 0:
    mask = torch.tensor([0., 1.]).view(1, 1, -1, 1).expand(N, C, -1, L)
else:
    mask = torch.tensor([1., 0.]).view(1, 1, -1, 1).expand(N, C, -1, L)

and multiply it with the activation.
Note that you would have to scale the activations during evaluation in case you want to disable this masking (or with the inverse during training).
In that case, you could wrap this operation in a custom nn.Module and use the internal self.training flag to switch between the training and evaluation behavior.