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 __call__(
      self,
      model,
      output_path: str = None,
      epoch: int = -1,
      steps: int = -1,
  ) -> float:
      if epoch != -1:
          if steps == -1:
              out_txt = " after epoch {}:".format(epoch)
          else:
              out_txt = " in epoch {} after {} steps:".format(
                  epoch, steps
              )
      else:
          out_txt = ":"
  
      logger.info(
          "CESoftmaxAccuracyEvaluator: Evaluating the model on "
          + self.name
          + " dataset"
          + out_txt
      )
      pred_scores = model.predict(
          self.sentence_pairs,
          convert_to_numpy=True,
          show_progress_bar=False,
      )
      pred_labels = np.argmax(pred_scores, axis=1)
  
      assert len(pred_labels) == len(self.labels)
  
      acc = np.sum(pred_labels == self.labels) / len(self.labels)
  
      logger.info("Accuracy: {:.2f}".format(acc * 100))
  
      if output_path is not None and self.write_csv:
          csv_path = os.path.join(output_path, self.csv_file)
          output_file_exists = os.path.isfile(csv_path)
          with open(
              csv_path,
              mode="a" if output_file_exists else "w",
              encoding="utf-8",
          ) as f:
              writer = csv.writer(f)
              if not output_file_exists:
                  writer.writerow(self.csv_headers)
  
              writer.writerow([epoch, steps, acc])
  
      return acc
  
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 3 for calling the CESoftmaxAccuracyEvaluator obj's underlying member method code with example usage: obj.__call__(model, output_path, epoch, steps) and returns: acc