5.1 Transformer may have wrong position embed

1. in"class Encoder": enc_outputs = self.src_emb(enc_inputs) + self.pos_emb(torch.LongTensor([[1,2,3,4,0]]))

I think it may be: enc_outputs = self.src_emb(enc_inputs) + self.pos_emb(torch.LongTensor([[0,1,2,3,4]]))

2. in"class Decoder": dec_outputs = self.tgt_emb(dec_inputs) + self.pos_emb(torch.LongTensor([[5,1,2,3,4]]))

I think it may be: dec_outputs = self.tgt_emb(dec_inputs) + self.pos_emb(torch.LongTensor([[0,1,2,3,4]]))

Locate the Encoder and Decoder classes in the relevant Jupyter Notebook and inspect how their positional-index tensors are constructed. Compare the current indices with the intended sequence-position convention, then run the notebook's Transformer example to verify the corrected behavior.

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