I think you would have to permute the activation from [seq, batch_size, features] to [batch_size, features, seq] via x = x.permute(1, 2, 0) and pass it to the batch norm layer.
Currently you are using the batch dimension to normalize the activations, which won’t work.
Alternatively, you could also set batch_first=True in your RNN, which should return the tensor as [batch_size, seq, features] and permute the last two dimensions,