class_docstr: Distilling the Knowledge in a Neural Network, arXiv:1503.02531 
code: def __init__(self, config):
      super().__init__(config)
      self.smoothing = self.config["solver"]["optimizer"][
          "label_smoothing"
      ]
      self.temperature = self.config["solver"]["distilling"][
          "temperature"
      ]
      self.alpha = self.config["solver"]["distilling"]["alpha"]
  
      assert self.alpha >= 0.0, "alpha : {}".format(self.alpha)
      assert self.alpha <= 1.0, "alpha : {}".format(self.alpha)
      assert self.temperature >= 1, "temperature : {}".format(
          self.temperature
      )
      self.T = tf.convert_to_tensor(self.temperature, dtype=tf.float32)
  
imports: import delta.compat
  from delta.utils.loss.base_loss import Loss
  from delta.utils.loss.loss_utils import cross_entropy
  from delta.utils.loss.loss_utils import mask_sequence_loss
  from delta.utils.loss.loss_utils import ctc_lambda_loss
  from delta.utils.loss.loss_utils import crf_log_likelihood
  from delta.utils.loss.loss_utils import focal_loss
  from delta.utils.register import registers
label_desc: Use image node_id 1 to create a new DistillationLoss object from inherited base classes: Loss with example: obj = DistillationLoss(config)