I’m unsure if the n dimension would already refer to the batch dimension or if it would be a seperate dimension (e.g. a temporal dimension).
Since PyTorch models expect inputs already containing a batch dimension, I would guess that the first dimension is already treated as the batch dim.
Depending on the used layers, you could use a DataLoader, specify a batch_size, and pass the data with the additional batch dimension to the model and check, if shape mismatch errors would be raised.