src/transformers/optimization_tf.py/WarmUp/__call__
class WarmUp:    def __call__(self, step):
        with tf.name_scope(self.name or "WarmUp") as name:
            # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
            # learning rate will be `global_step/num_warmup_steps * init_lr`.
            global_step_float = tf.cast(step, tf.float32)
            warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
            warmup_percent_done = global_step_float / warmup_steps_float
            warmup_learning_rate = self.initial_learning_rate * tf.math.pow(warmup_percent_done, self.power)
            return tf.cond(
                global_step_float < warmup_steps_float,
                lambda: warmup_learning_rate,
                lambda: self.decay_schedule_fn(step - self.warmup_steps),
                name=name,
            )