output tensors are provided as (batch, seq, feature). Default:
        "False" (seq, batch, feature).
  * **norm_first** (*bool*) -- if "True", encoder and decoder
    layers will perform LayerNorms before other attention and
    feedforward operations, otherwise after. Default: "False"
    (after).

Examples::
      >>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12)
      >>> src = torch.rand((10, 32, 512))
      >>> tgt = torch.rand((20, 32, 512))
      >>> out = transformer_model(src, tgt)
Note: A full example to apply nn.Transformer module for the word
   language model is available in
   https://github.com/pytorch/examples/tree/master/word_language_model
forward(src, tgt, src_mask=None, tgt_mask=None, memory_mask=None, src_key_padding_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)
  Take in and process masked source/target sequences.

  Parameters:
     * **src** (*Tensor*) -- the sequence to the encoder
