Upgrade the AI model that is designed to predict the next value of a graph based on what it is now. The model should be designed in such a way, where it can periodically quickly train on new data to adapt and provide even more accurate outputs.

# 1. Define the custom Multi-Head Attention layer (optional enhancement)
class CustomMultiHeadAttention(Layer):
    def __init__(self, embed_dim, num_heads=4, **kwargs):
        super(CustomMultiHeadAttention, self).__init__(**kwargs)
        self.mha = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.layer_norm = LayerNormalization()
        self.dropout = Dropout(0.1)

    def call(self, inputs, training=False):
        attn_output = self.mha(inputs, inputs)  # Self-attention
        attn_output = self.dropout(attn_output, training=training)
        out = self.layer_norm(inputs + attn_output)  # Residual connection and layer norm
        return out

    def get_config(self):
        config = super(CustomMultiHeadAttention, self).get_config()
        config.update({
            "mha": self.mha,
            "layer_norm": self.layer_norm,
            "dropout": self.dropout
        })
        return config

# 2. Define the enhanced Attention layer (multi-head attention instead of single)
class EnhancedAttention(Layer):
    def __init__(self, embed_dim, num_heads=4, **kwargs):
        super(EnhancedAttention, self).__init__(**kwargs)
        self.mha = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.layer_norm = LayerNormalization()
        self.dropout = Dropout(0.1)

    def call(self, x, training=False):
        attn_output = self.mha(x, x)  # Self-attention
        attn_output = self.dropout(attn_output, training=training)
        out = self.layer_norm(x + attn_output)  # Residual connection and layer norm
        return out

    def get_config(self):
        config = super(EnhancedAttention, self).get_config()
        config.update({
            "mha": self.mha,
            "layer_norm": self.layer_norm,
            "dropout": self.dropout
        })
        return config

# 3. Define the Positional Encoding (optional, useful for transformer models)
class PositionalEncoding(Layer):
    def __init__(self, maxlen, embed_dim, **kwargs):
        super(PositionalEncoding, self).__init__(**kwargs)
        self.pos_encoding = self.positional_encoding(maxlen, embed_dim)

    def get_config(self):
        config = super(PositionalEncoding, self).get_config()
        return config

    def positional_encoding(self, maxlen, embed_dim):
        pos = np.arange(maxlen)[:, np.newaxis]
        i = np.arange(embed_dim)[np.newaxis, :]
        angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(embed_dim))
        angle_rads = pos * angle_rates

        # Apply sin to even indices in the array
        angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])

        # Apply cos to odd indices in the array
        angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])

        pos_encoding = angle_rads[np.newaxis, ...]
        return tf.cast(pos_encoding, dtype=tf.float32)

    def call(self, x):
        return x + self.pos_encoding[:, :tf.shape(x)[1], :]

# 4. Define the upgraded model creation function
def create_model(input_shape, embed_dim=64, num_heads=4):
    inputs = Input(shape=(input_shape[0], input_shape[1]))
    
    # Positional Encoding (optional)
    x = PositionalEncoding(maxlen=input_shape[0], embed_dim=input_shape[1])(inputs)
    
    # First Bidirectional LSTM layer
    x = Bidirectional(LSTM(160, return_sequences=True))(x)
    x = Dropout(0.3)(x)
    x = LayerNormalization()(x)
    
    # Second Bidirectional LSTM layer with residual connection
    x2 = Bidirectional(LSTM(160, return_sequences=True))(x)
    x2 = Dropout(0.3)(x2)
    x2 = Add()([x, x2])  # Residual connection
    x2 = LayerNormalization()(x2)
    
    # Transformer Encoder Layer
    x2 = CustomMultiHeadAttention(embed_dim=160 * 2, num_heads=num_heads)(x2)
    x2 = Dropout(0.3)(x2)
    
    # Third Bidirectional LSTM layer
    x3 = Bidirectional(LSTM(80, return_sequences=True))(x2)
    x3 = Dropout(0.3)(x3)
    x3 = LayerNormalization()(x3)
    
    # Enhanced Attention mechanism (Multi-Head)
    attention_output = EnhancedAttention(embed_dim=80 * 2, num_heads=num_heads)(x3)
    
    # Global Average Pooling
    x_avg = GlobalAveragePooling1D()(attention_output)
    # Global Max Pooling
    x_max = tf.keras.layers.GlobalMaxPooling1D()(attention_output)
    # Concatenate pooling layers
    x = tf.keras.layers.Concatenate()([x_avg, x_max])
    
    x = Dropout(0.3)(x)
    
    # Fully connected dense layers with regularization
    x = Dense(128, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(1e-4))(x)
    x = Dropout(0.3)(x)
    x = Dense(64, activation="relu", kernel_regularizer=tf.keras.regularizers.l2(1e-4))(x)
    x = Dropout(0.3)(x)
    
    # Output layer
    outputs = Dense(1, activation="linear")(x)
    
    # Model creation
    model = Model(inputs=inputs, outputs=outputs)
    
    # Compile the model with a learning rate scheduler
    optimizer = Adam(learning_rate=0.001)
    model.compile(
        loss=Huber(delta=1.0),
        optimizer=optimizer,
        metrics=["mae"]
    )
    
    return model