Remove test code

    import torch

    return torch.optim.lr_scheduler.LambdaLR(optimizer, f)

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class ReduceLRonPlateau(LearningRateSchedule):

  """Reduces the Learning Rate when the chosen metric has stopped improving."""

  def __init__(self,

               initial_rate: float,

               decay_steps: int,

               alpha: float = 0.0,

               beta: float = 0.001,

               num_periods: float = 0.5):

    """

    Parameters

    ----------

    learning_rate : float

    initial learning rate

    decay_steps : int

    number of steps to decay over

    num_periods : number of periods in the cosine part of the decay

    """

    self.initial_rate = initial_rate

    self.decay_steps = decay_steps

    self.alpha = alpha

    self.beta = beta

    self.num_periods = num_periods

  def _create_tf_tensor(self, global_step):

    import tensorflow as tf

    return tf.compat.v1.train.linear_cosine_decay(

        learning_rate=self.initial_rate,

        global_step=global_step,

        decay_steps=self.decay_steps,

        alpha=self.alpha,

        beta=self.beta,

        num_periods=self.num_periods)

  def _create_pytorch_schedule(self, optimizer):

    def f(step):

      t = min(step, self.decay_steps) / self.decay_steps

      linear_decay = 1 - t

      cosine_decay = 0.5 * (1 + math.cos(math.pi * 2 * self.num_periods * t))

      decayed = (self.alpha + linear_decay) * cosine_decay + self.beta

      return self.initial_rate * decayed

    import torch

    return torch.optim.lr_scheduler.LambdaLR(optimizer, f)