class_docstr: Computes the mean squared error (x100) between the computed sentence embedding and some target sentence embedding.
  
  :param dataframe: It must have the following format. Rows contains different, parallel sentences.
      Columns are the respective language codes::
  
          [{'en': 'My sentence', 'es': 'Sentence in Spanisch', 'fr': 'Sentence in French'...},
           {'en': 'My second sentence', ...}]
  :param combinations: Must be of the format ``[('en', 'es'), ('en', 'fr'), ...]``.
      First entry in a tuple is the source language. The sentence in the respective language will be fetched from
      the dataframe and passed to the teacher model. Second entry in a tuple the the target language. Sentence
      will be fetched from the dataframe and passed to the student model
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code: def __call__(
      self,
      model,
      output_path: str = None,
      epoch: int = -1,
      steps: int = -1,
  ):
      model.eval()
  
      mse_scores = []
      for src_lang, trg_lang in self.combinations:
          src_sentences, trg_sentences = self.data[(src_lang, trg_lang)]
  
          src_embeddings = np.asarray(
              [self.teacher_embeddings[sent] for sent in src_sentences]
          )
          trg_embeddings = np.asarray(
              model.encode(trg_sentences, batch_size=self.batch_size)
          )
  
          mse = ((src_embeddings - trg_embeddings) ** 2).mean()
          mse *= 100
          mse_scores.append(mse)
  
          logger.info(
              "MSE evaluation on {} dataset - {}-{}:".format(
                  self.name, src_lang, trg_lang
              )
          )
          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_scores)
  
      return -np.mean(mse_scores)
  
file_path: sentence-transformers/sentence_transformers/evaluation/MSEEvaluatorFromDataFrame.py
imports: from sentence_transformers.evaluation import SentenceEvaluator
  from sentence_transformers import SentenceTransformer
  from typing import List, Tuple, Dict
  import numpy
  import logging
  import os
  import csv
label_desc: Use image node_id 2 for calling the MSEEvaluatorFromDataFrame obj's underlying member method code with example usage: obj.__call__(model, output_path, epoch, steps) and returns: unknown