class_docstr: This evaluator can be used with the CrossEncoder class.
  
  It is designed for CrossEncoders with 2 or more outputs. It measure the
  accuracy of the predict class vs. the gold labels.
code: def from_input_examples(cls, examples: List[InputExample], **kwargs):
      sentence_pairs = []
      labels = []
  
      for example in examples:
          sentence_pairs.append(example.texts)
          labels.append(example.label)
      return cls(sentence_pairs, labels, **kwargs)
  
file_path: sentence-transformers/sentence_transformers/cross_encoder/evaluation/CESoftmaxAccuracyEvaluator.py
imports: import logging
  import os
  import csv
  from typing import List
  from ...None import InputExample
  import numpy
label_desc: Use image node_id 2 for calling the CESoftmaxAccuracyEvaluator obj's underlying member method code with example usage: obj.from_input_examples(cls, examples) and returns: cls