def __init__(
        self,
        model,
        update_after_step=0,
        inv_gamma=1.0,
        power=2 / 3,
        min_value=0.0,
        max_value=0.9999
    ):
        """
        @crowsonkb's notes on EMA Warmup:
            If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
            to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
            gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
            at 215.4k steps).
        Args:
            inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
            power (float): Exponential factor of EMA warmup. Default: 2/3.
            min_value (float): The minimum EMA decay rate. Default: 0.
        """

        self.averaged_model = model
        self.averaged_model.eval()
        self.averaged_model.requires_grad_(False)

        self.update_after_step = update_after_step
        self.inv_gamma = inv_gamma
        self.power = power
        self.min_value = min_value
        self.max_value = max_value

        self.decay = 0.0
        self.optimization_step = 0

        @crowsonkb's notes on EMA Warmup:
            If gamma=1 and power=1, implements a simple average. gamma=1, power=2/3 are good values for models you plan
            to train for a million or more steps (reaches decay factor 0.999 at 31.6K steps, 0.9999 at 1M steps),
            gamma=1, power=3/4 for models you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999
            at 215.4k steps).
        Args:
            inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1.
            power (float): Exponential factor of EMA warmup. Default: 2/3.
            min_value (float): The minimum EMA decay rate. Default: 0.
        
policy/RDT/models/ema_model.py