# TensorFlow specific implementations will go here

import tensorflow as tf
import keras

# Example model using TensorFlow
class TensorFlowModel(keras.Model):
    def __init__(self, features):
        super(TensorFlowModel, self).__init__()
        self.layers_ = keras.Sequential([
            keras.layers.Dense(100, activation='relu'),
            keras.layers.Dense(features),
        ])

    def call(self, inputs):
        return self.layers_(inputs)

# Training function
@tf.function
def train_tf_model(model, X, y, epochs=10, lr=0.001):
    optimizer = keras.optimizers.Adam(learning_rate=lr)
    loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)

    @tf.function
    def train_step(x, y):
        with tf.GradientTape() as tape:
            logits = model(x, training=True)
            loss = loss_fn(y, logits)
        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
        return loss

    for epoch in range(epochs):
        loss = train_step(X, y)
        if epoch % 10 == 0:
            print(f"Epoch {epoch}, Loss: {loss.numpy()}")

    return model

# Decorator for distributed training
def distribute(strategy):
    def decorator(func):
        def wrapper(*args, **kwargs):
            return strategy.run(func, args=args, kwargs=kwargs)
        return wrapper
    return decorator

# Example usage of distribute decorator
# @distribute(tf.distribute.MirroredStrategy())
# def distributed_train_step(model, x, y):
#     # Your distributed training logic here
#     pass
