Look at this code and tell me what is wrong about it: import tensorflow as tf
from keras.layers import TextVectorization, Embedding
from keras.models import Sequential
from keras.layers.experimental.preprocessing import StringLookup

# Define some constants
MAX_TOKENS = 250  # Size of the vocabulary
EMBED_DIM = 64      # Embedding size
NUM_HEADS = 4       # Number of attention heads
FF_DIM = 64         # Hidden layer size in feed forward network
MAX_LEN = 40        # Maximum length of sentence
BATCH_SIZE = 64     # Training batch size

# Prepare your dataset text file
text_file_path = "dataset.txt"

# Load the text data
with open(text_file_path, "r", encoding="utf-8") as f:
    text_data = f.read()

# Split the dataset into lines
lines = text_data.split("\n")

# Define the text vectorization layer
vectorize_layer = TextVectorization(
    max_tokens=MAX_TOKENS,
    output_mode="int",
    output_sequence_length=MAX_LEN
)

# Adapt it to the dataset
vectorize_layer.adapt(lines)

# Define the Transformer block as a layer
def transformer_encoder(inputs, embed_dim, num_heads, ff_dim, rate=0.1):
    # Multi-headed self-attention
    attention_output = tf.keras.layers.MultiHeadAttention(
        num_heads=num_heads, key_dim=embed_dim
    )(inputs, inputs)
    attention_output = tf.keras.layers.Dropout(rate)(attention_output)
    attention_output = tf.keras.layers.LayerNormalization(epsilon=1e-6)(inputs + attention_output)

    # Feed-forward layer
    ff_output = tf.keras.Sequential(
        [
            tf.keras.layers.Dense(ff_dim, activation="relu"),
            tf.keras.layers.Dense(embed_dim),
        ]
    )(attention_output)
    ff_output = tf.keras.layers.Dropout(rate)(ff_output)
    ff_output = tf.keras.layers.LayerNormalization(epsilon=1e-6)(attention_output + ff_output)
    return ff_output

# Define the token and position embedding layer
def token_and_position_embedding(x, maxlen, vocab_size, embed_dim):
    token_embeddings = Embedding(input_dim=vocab_size, output_dim=embed_dim)(x)
    positions = tf.range(start=0, limit=maxlen, delta=1)
    position_embeddings = Embedding(input_dim=maxlen, output_dim=embed_dim)(positions)
    return token_embeddings + position_embeddings

# Transformer model with Keras
def create_transformer_model(maxlen, vocab_size, embed_dim, num_heads, ff_dim):
    inputs = tf.keras.Input(shape=(maxlen,))
    embedding_layer = token_and_position_embedding(inputs, maxlen, vocab_size, embed_dim)
    transformer_block_output = transformer_encoder(embedding_layer, embed_dim, num_heads, ff_dim)
    outputs = tf.keras.layers.GlobalAveragePooling1D()(transformer_block_output)
    outputs = tf.keras.layers.Dropout(0.1)(outputs)
    outputs = tf.keras.layers.Dense(vocab_size, activation="softmax")(outputs)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

# Create the model
transformer_model = create_transformer_model(MAX_LEN, MAX_TOKENS, EMBED_DIM, NUM_HEADS, FF_DIM)

# Compile the model
transformer_model.compile(optimizer="adam", loss="sparse_categorical_crossentropy")

# Prepare the dataset for training
text_inputs = vectorize_layer(lines[:-1])
text_labels = vectorize_layer(lines[1:])
dataset = tf.data.Dataset.from_tensor_slices((text_inputs, text_labels))
dataset = dataset.shuffle(1024).batch(BATCH_SIZE)

# Train the model
transformer_model.fit(dataset, epochs=10)

# Save the model
transformer_model.save("STM-Experiment.keras")

print("Model training completed and saved.")