It depends, what you want to achieve.
The original mask before the unsqueeze and expand operations can be used to index x directly, which would yield:
x = torch.ones([1, 167, 128])
batch_size, length, features = x.size()
p = 0.5
mask = torch.distributions.Bernoulli(
    probs=(1 - p)).sample((batch_size, length)).to(x.device)
mask = ~mask.bool()
print(mask.shape)
> torch.Size([1, 167])
print(x[mask].shape)
> torch.Size([81, 128])
print(mask.sum())
> tensor(81)

So would you like to replace all these 81 values in dim1 with the sum of them?