MultiheadAttention
class torch.nn.quantizable.MultiheadAttention(embed_dim, num_heads, dropout=0.0, bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, vdim=None, batch_first=False, device=None, dtype=None)
dequantize()
  Utility to convert the quantized MHA back to float.

  The motivation for this is that it is not trivial to conver the
  weights from the format that is used in the quantized version
  back to the float.

forward(query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None, average_attn_weights=True, is_causal=False)
  Note::
     Please, refer to "forward()" for more information

  Parameters:
     * **query** (*Tensor*) -- map a query and a set of key-value
       pairs to an output. See "Attention Is All You Need" for
       more details.

     * **key** (*Tensor*) -- map a query and a set of key-value
       pairs to an output. See "Attention Is All You Need" for
       more details.
