class_docstr: This evaluator can be used with the CrossEncoder class. Given sentence pairs and continuous scores,
  it compute the pearson & spearman correlation between the predicted score for the sentence pair
  and the gold score.
code: def from_input_examples(cls, examples: List[InputExample], **kwargs):
      sentence_pairs = []
      scores = []
  
      for example in examples:
          sentence_pairs.append(example.texts)
          scores.append(example.label)
      return cls(sentence_pairs, scores, **kwargs)
  
file_path: sentence-transformers/sentence_transformers/cross_encoder/evaluation/CECorrelationEvaluator.py
imports: import logging
  from scipy.stats import pearsonr, spearmanr
  from typing import List
  import os
  import csv
  from ...None import InputExample
label_desc: Use image node_id 2 for calling the CECorrelationEvaluator obj's underlying member method code with example usage: obj.from_input_examples(cls, examples) and returns: cls