normalized_train_dataset = (
    train_data
    .map(lambda img,label: img_normalize_one_hot(img,label,config),num_parallel_calls=tf.data.AUTOTUNE)
    .batch(config["batch_size"])
    .cache()
    .shuffle(buffer_size=config["buffer_size"])
    .prefetch(buffer_size=tf.data.AUTOTUNE)
)
normalized_valid_dataset = (
    test_data.map(lambda img,label : img_normalize_one_hot(img,label,config),num_parallel_calls=tf.data.AUTOTUNE)
    .batch(config["batch_size"])
    .cache()
    .shuffle(buffer_size=config["buffer_size"])
    .prefetch(buffer_size=tf.data.AUTOTUNE)
)
normalized_test_dataset = (valid_data.map(lambda img,label : img_normalize_one_hot(img,label,config),num_parallel_calls=tf.data.AUTOTUNE)
    .batch(config["batch_size"])
    .prefetch(buffer_size=tf.data.AUTOTUNE)
                                          )

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