class_docstr: Computes the mean squared error (x100) between the computed sentence embedding
  and some target sentence embedding.
  
  The MSE is computed between ||teacher.encode(source_sentences) - student.encode(target_sentences)||.
  
  For multilingual knowledge distillation (https://arxiv.org/abs/2004.09813), source_sentences are in English
  and target_sentences are in a different language like German, Chinese, Spanish...
  
  :param source_sentences: Source sentences are embedded with the teacher model
  :param target_sentences: Target sentences are ambedding with the student model.
  :param show_progress_bar: Show progress bar when computing embeddings
  :param batch_size: Batch size to compute sentence embeddings
  :param name: Name of the evaluator
  :param write_csv: Write results to CSV file
code: def __call__(self, model, output_path, epoch=-1, steps=-1):
      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 = ":"
  
      target_embeddings = model.encode(
          self.target_sentences,
          show_progress_bar=self.show_progress_bar,
          batch_size=self.batch_size,
          convert_to_numpy=True,
      )
  
      mse = ((self.source_embeddings - target_embeddings) ** 2).mean()
      mse *= 100
  
      logger.info(
          "MSE evaluation (lower = better) on "
          + self.name
          + " dataset"
          + out_txt
      )
      logger.info("MSE (*100):\t{:4f}".format(mse))
  
      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,
              newline="",
              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, mse])
  
      return -mse
  
file_path: sentence-transformers/sentence_transformers/evaluation/MSEEvaluator.py
imports: from sentence_transformers.evaluation import SentenceEvaluator
  import logging
  import os
  import csv
  from typing import List
label_desc: Use image node_id 2 for calling the MSEEvaluator obj's underlying member method code with example usage: obj.__call__(model, output_path, epoch, steps) and returns: unknown