ations / self.step_size - 2 * cycle + 1)
        if self.scale_mode == &#34;cycle&#34;:
            return self.base_lr + (self.max_lr - self.base_lr) * np.maximum(
                0, (1 - x)
            ) * self.scale_fn(cycle)
        else:
            return self.base_lr + (self.max_lr - self.base_lr) * np.maximum(
                0, (1 - x)
            ) * self.scale_fn(self.clr_iterations)

    def on_train_begin(self, logs={}):
        logs = logs or {}

        if self.clr_iterations == 0:
          