Add&Norm layer is missing after each attention layer

Hi,
According to the transformer architecture, there should be a add&norm layer after each attention layer. However for code in /docs/tutorials/transformer.ipynb, those Add&Norm layers are missing.

Take decoder layer for example, the original code is:

  def call(self, x, context):
      x = self.causal_self_attention(x=x)
      x = self.cross_attention(x=x, context=context)

      \# Cache the last attention scores for plotting later
      self.last_attn_scores = self.cross_attention.last_attn_scores

      x = self.ffn(x)  # Shape `(batch_size, seq_len, d_model)`.
  return x

If we add Add&Norm layers, it should be:

  def call(self, x, context):
      x = self.add([x, self.causal_self_attention(x=x)])
      x = self.layer_norm1(x)

      x = self.add([x, self.cross_attention(x=x, context=context)])
      x = self.layer_norm2(x)

      \# Cache the last attention scores for plotting later
      self.last_attn_scores = self.cross_attention.last_attn_scores

      x = self.ffn(x)  # Shape `(batch_size, seq_len, d_model)`.
  return x

I wonder if those add&norm layers are ignored on purpose.

Open /docs/tutorials/transformer.ipynb and inspect the encoder and decoder layer call methods around the attention operations. Compare the tutorial with the transformer architecture shown in the issue and determine whether the missing Add&Norm steps are intentional. Done means the tutorial reflects the intended architecture and the notebook still runs successfully.