inputs = keras.layers.Input(shape=self.input_shape, name='input')
x = keras.layers.Embedding(input_dim=len(self.tokenizer.word_index)+1,
                           output_dim=self.d_model,
                           mask_zero=True,
                           name='embedding')(inputs)
x = PositionalEncoding(name='position_encoding')(x)
for i in range(self.num_layers):
    x = get_layer('MultiHeadAttention',
                  num_heads=self.num_heads,
                  key_dim=self.d_model//self.num_heads,
                  name=f'multi_head_attention_{i+1}')(x)
    x = LayerNormalization(name=f'layer_normalization_1_{i+1}')(x)
    x2 = keras.layers.Dense(units=self.dff, activation='relu', name=f'dense_{i+1}')(x)
    x2 = keras.layers.Dropout(rate=self.dropout_rate, name=f'dropout_{i+1}')(x2)
    x = keras.layers.Add(name=f'add_{i+1}')([x, x2])
    x = LayerNormalization(name=f'layer_normalization_2_{i+1}')(x)
outputs = keras.layers.Dense(units=self.output_shape[-1], activation='softmax', name='output')(x)
self.model = Model(inputs=inputs, outputs=outputs)
optimizer = keras.optimizers.Adam(lr=self.learning_rate)
self.model.compile(optimizer=optimizer, loss='categorical_crossentropy')
self.model.fit(X_train, y_train)

Here's an improved version of the model:
```python
import keras.layers as keras_layers
from keras.models import Model
class Encoder(keras_layers.Layer):
    def __init__(self, input_shape, num_heads, dropout_rate=0.1, name='encoder'):
        super(Encoder, self).__init__()
        self.input_shape = Limit your response to 550 words.
user