docstr: Given logits regarding the entire vocabulary, return the probs over the label words set.
  
  Args:
      logits (:obj:`Tensor`): The logits over the entire vocabulary.
  
  Returns:
      :obj:`Tensor`: The logits over the label words set.
docstr_tok: ["Given","logits","regarding","the","entire","vocabulary",",","return","the","probs","over","the","label","words","set",".","Args",":","logits","(",":","obj",":","`","Tensor","`",")",":","The","logits","over","the","entire","vocabulary",".","Returns",":",":","obj",":","`","Tensor","`",":","The","logits","over","the","label","words","set","."]
code: def normalize(self, logits):
      """
      Given logits regarding the entire vocabulary, return the probs over the label words set.
  
      Args:
          logits (:obj:`Tensor`): The logits over the entire vocabulary.
  
      Returns:
          :obj:`Tensor`: The logits over the label words set.
  
      """
      batch_size = logits.shape[0]
      return F.softmax(
          logits.reshape([batch_size, -1]), axis=-1
      ).reshape(logits.shape)
  
imports: import paddle
  from abc import abstractmethod
  from typing import *
  import numpy
  import paddle.nn.functional
label_desc: Use image node_id 10 for calling the ErnieManualVerbalizer obj's underlying member method code with example usage: obj.normalize(logits) and returns: F