Fix any bugs found in this code, and you job is to return the corrected and fully working version of the code you will receive, don't waste your output tokens with text or anything outside of code, just code, here is the code: import tensorflow as tf
import numpy as np

# Assuming the same hyperparameters defined as in the PyTorch code
# hyperparameters
batch_size = 96
block_size = 20
max_iters = 200
eval_interval = 100
learning_rate = 1e-3
device = 'cuda' if tf.test.is_gpu_available() else 'cpu'
eval_iters = 200
n_embd = 16
n_head = 2
n_layer = 2
dropout = 0.0

hidden_nodes = 20

# Set random seed for reproducibility
tf.random.set_seed(1337)

# Load text data and preprocess as before
with open('data/universe.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# Prepare data
data = np.array([ord(c) for c in text], dtype=np.int32)
n = int(0.9 * len(data))
train_data = data[:n]
val_data = data[n:]

# data loading function
@tf.function
def get_batch(split):
    data = train_data if split == 'train' else val_data
    ix = np.random.randint(len(data) - block_size, size=(batch_size,))
    x = np.array([data[i:i + block_size] for i in ix])
    y = np.array([data[i + 1:i + block_size + 1] for i in ix])
    return x, y

# TensorFlow model definition
class GPTLanguageModel(tf.keras.Model):
    def __init__(self, vocab_size, n_embd, block_size, n_head, n_layer, dropout_rate=0.1):
        super(GPTLanguageModel, self).__init__()
        self.token_embedding_table = tf.keras.layers.Embedding(vocab_size, n_embd)
        self.position_embedding_table = tf.keras.layers.Embedding(block_size, n_embd)
        self.blocks = [self._build_block(n_embd, n_head) for _ in range(n_layer)]
        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=1e-6)
        self.lm_head = tf.keras.layers.Dense(vocab_size)

    def _build_block(self, n_embd, n_head):
        head_size = n_embd // n_head + 1  # Fix head_size calculation
        return tf.keras.Sequential([
            tf.keras.layers.LayerNormalization(epsilon=1e-6),
            tf.keras.layers.MultiHeadAttention(num_heads=n_head, key_dim=head_size),
            tf.keras.layers.Dense(4 * n_embd, activation='relu'),
            tf.keras.layers.Dense(n_embd)
        ])

    def call(self, idx, targets=None, training=False):
        B, T = tf.shape(idx)[0], tf.shape(idx)[1]
        tok_emb = self.token_embedding_table(idx)
        pos_emb = self.position_embedding_table(tf.range(T))
        x = tok_emb + pos_emb[None, :, :]

        for block in self.blocks:
            x = block(x, training=training)

        x = self.ln_f(x)
        logits = self.lm_head(x)

        loss = None
        if targets is not None:
            loss = self.compute_loss(logits, targets)

        return logits, loss

    def compute_loss(self, logits, targets):
        loss = tf.keras.losses.sparse_categorical_crossentropy(targets, logits, from_logits=True)
        return tf.reduce_mean(loss)

    def generate(self, idx, max_new_tokens):
        for _ in range(max_new_tokens):
            logits, _ = self(idx[:, -block_size:], training=False)
            logits = logits[:, -1, :]
            idx_next = tf.random.categorical(logits, num_samples=1, dtype=tf.int32)
            idx = tf.concat([idx, idx_next], axis=1)
        return idx

vocab_size = len(np.unique(data))

model = GPTLanguageModel(vocab_size, n_embd, block_size, n_head, n_layer, dropout_rate=dropout)

# Define the optimizer
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

# Define the metrics to track during training
train_loss_metric = tf.keras.metrics.Mean(name='train_loss')
val_loss_metric = tf.keras.metrics.Mean(name='val_loss')

# Define the training loop
@tf.function
def train_step(inputs, targets):
    with tf.GradientTape() as tape:
        logits, loss = model(inputs, targets, training=True)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    train_loss_metric(loss)

@tf.function
def val_step(inputs, targets):
    logits, loss = model(inputs, targets, training=False)
    val_loss_metric(loss)

# Training loop
for iteration in range(max_iters):
    # Training
    for _ in range(eval_interval):
        inputs, targets = get_batch('train')
        train_step(inputs, targets)

    # Validation
    for _ in range(eval_iters):
        inputs, targets = get_batch('val')
        val_step(inputs, targets)

    # Print training and validation loss
    if (iteration + 1) % eval_interval == 0:
        train_loss = train_loss_metric.result()
        val_loss = val_loss_metric.result()
        print(f'Iteration {iteration + 1}: Train Loss = {train_loss:.4f}, Val Loss = {val_loss:.4f}')

        # Reset the metrics
        train_loss_metric.reset_states()
        val_loss_metric.reset_states()

# Save the trained model
model.save('Universe-GPT-tf.keras')