Need help to understand the logic here about keras

[sachinprasadhs]

Below is the same code from the keras example, I think you might have got confused between the multiple occurrence of decoder_outputs.
First decoder_outputs = layers.Dense(vocab_size, activation='softmax')(x) is part of the Softmax in the decoder part( refer architecture below)
Second decoder_outputs = decoder([decoder_inputs, encoder_outputs]) is the part where the output is formed with the decoder_inputs and encoder_outputs which will have the encoder context (follow arrow flowing from encoder part to decoder from below architecture)

x = layers.Dropout(0.5)(x)
decoder_outputs = layers.Dense(vocab_size, activation="softmax")(x)
decoder = keras.Model([decoder_inputs, encoded_seq_inputs], decoder_outputs)

decoder_outputs = decoder([decoder_inputs, encoder_outputs])
transformer = keras.Model(
    [encoder_inputs, decoder_inputs], decoder_outputs, name="transformer"
)

Architecture of the transformer: