Hello,
In the 60 minutes blitz tutorial 30, it is written that:

torch.nn only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, nn.Conv2d will take in a 4D Tensor of nSamples x nChannels x Height x Width . If you have a single sample, just use input.unsqueeze(0) to add a fake batch dimension.

However, in the RNN classification tutorial 61, the batch size is in the first dimension:

To make a word we join a bunch of those into a 2D matrix <line_length x 1 x n_letters> . That extra 1 dimension is because PyTorch assumes everything is in batches - we’re just using a batch size of 1 here.

But when looking at the documentation of nn.linear, it seems that the batch size (N?) is in the first position, as in Conv2d.
Have I misunderstood something, or is there an error in the RNN tutorial ?
Thanks in advance for your insights.