code: def calibrate(self, label_words_probs, **kwargs):
      shape = label_words_probs.shape
      assert (
          self._calibrate_logits.dim() == 1
      ), "self._calibrate_logits are not 1-d tensor"
      calibrate_label_words_probs = self.normalize(
          self.project(self._calibrate_logits.unsqueeze(0), **kwargs)
      )
      assert (
          calibrate_label_words_probs.shape[1:]
          == label_words_probs.shape[1:]
          and calibrate_label_words_probs.shape[0] == 1
      ), "shape not match"
      label_words_probs /= calibrate_label_words_probs + 1e-15
      # normalize # TODO Test the performance
      norm = label_words_probs.reshape(shape[0], -1).sum(
          dim=-1, keepdim=True
      )  # TODO Test the performance of detaching()
      label_words_probs = label_words_probs.reshape(shape[0], -1) / norm
      label_words_probs = label_words_probs.reshape(*shape)
      return label_words_probs
  
imports: import paddle
  from abc import abstractmethod
  from typing import *
  import numpy
  import paddle.nn.functional
label_desc: Use image node_id 4 for calling the ErnieManualVerbalizer obj's underlying member method code with example usage: obj.calibrate(label_words_probs) and returns: label_words_probs