I have sequence data going in for RNN type architecture with batch first i.e. my input data to the model will be of dimension 64x256x16 (64 is the batch size, 256 is the sequence length and 16 features) and coming output is 64x256x1024 (again 64 is the batch size, 256 is the sequence length and 1024 features). Now, if I want to apply batch normalization should it not be on output features which is 1024 but the problem is pytorch does not allow this or I do not completely understand.  From this doc 13 it is clear from the following statement
`torch.nn.BatchNorm1d`: 
Parameters: num_features – C from an expected input of size (N,C,L) or
                           L from input of size (N,L)

it is clear for 2D data that batch-normalization is executed on L for input size(N, L) as N is incoming features to the layer and L is outgoing features but it is confusing for 3D data which I believe should also be L.
Please someone who has used batch-normalization for 3D data.
Any help is very much appreciated.
Thank you for all the help.