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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_beta(self, kl_loss=0.0):
 """Get the KL multiplier, either dynamically or schedule based. if hparams.latent_loss_multiplier_dynamic is set to true, then beta is being adjusted to keep KL under hparams.latent_loss_multiplier_epsilon. In order to do so, the beta is being updated at each iteration by taking steps of size hparams.latent_loss_multiplier_alpha. The same formulation can be retrieved by solving the Lagrangian with KL < epsilon as a constraint. Args: kl_loss: KL loss. Only used for dynamic adjustment. Returns: beta: the final value of beta. """

    if self.hparams.latent_loss_multiplier_dynamic:
      beta = tf.Variable(self.hparams.latent_loss_multiplier,
                         trainable=False, dtype=tf.float32)
      alpha = self.hparams.latent_loss_multiplier_alpha
      epsilon = self.hparams.latent_loss_multiplier_epsilon
      shadow_beta = beta + alpha * (kl_loss - epsilon)
      # Caping the beta between 0 and 1. May need to change this later on.
      shadow_beta = tf.maximum(shadow_beta, 0.0)
      shadow_beta = tf.minimum(shadow_beta, 1.0)
      update_op = tf.assign(beta, shadow_beta)
    else:
      beta = common_video.beta_schedule(
          schedule=self.hparams.latent_loss_multiplier_schedule,
          global_step=self.get_iteration_num(),
          final_beta=self.hparams.latent_loss_multiplier,
          decay_start=(self.hparams.num_iterations_1st_stage +
                       self.hparams.num_iterations_2nd_stage),
          decay_end=self.hparams.anneal_end)
      update_op = tf.identity(beta)  # fake update for regular beta.
    with tf.control_dependencies([update_op]):
      tf.summary.scalar("beta", beta)
      return beta