[D] Transformer multi-head attention implementation

I've been following [Annotated Transformer](https://nlp.seas.harvard.edu/annotated-transformer/) to implement the transformer architecture. In the multi-head attention class's `forward()` method, Query, Key & Value are being multiplied with corresponding projection matrix; `W_q, W_k, W_v`.

query, key, value = [
lin(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
for lin, x in zip(self.linears, (query, key, value))
]

Here, `lin(x)` is being reshaped into `(nbatches, -1, self.h, self.d_k)`and dimension 1 & 2 is being transposed which makes the dimension `(nbatches, self.h, -1, self.d_k).`

I'm failing to understand why …

key machinelearning query value view zip