class_docstr: A loader and saver for pretrained embeddings. 
code: def read_pretrain(self):
      # load from pretrained filename
      if self._vec_filename is not None:
          words, emb, failed = self.read_from_file(
              self._vec_filename, self._max_vocab
          )
      elif self._csv_filename is not None:
          words, emb = self.read_from_csv(self._csv_filename)
      else:
          raise RuntimeError("Vector file is not provided.")
  
      if len(emb) - len(VOCAB_PREFIX) != len(words):
          raise RuntimeError(
              "Loaded number of vectors does not match number of words."
          )
  
      # Use a fixed vocab size
      if self._max_vocab > len(VOCAB_PREFIX) and self._max_vocab < len(
          words
      ) + len(VOCAB_PREFIX):
          words = words[: self._max_vocab - len(VOCAB_PREFIX)]
          emb = emb[: self._max_vocab]
  
      vocab = PretrainedWordVocab(words)
  
      return vocab, emb
  
imports: import csv
  import os
  import re
  import lzma
  import logging
  import numpy
  import torch
  from .vocab import BaseVocab, VOCAB_PREFIX, UNK_ID
  from stanza.models.common.utils import open_read_binary, open_read_text
  from stanza.resources.common import DEFAULT_MODEL_DIR
label_desc: Use image node_id 7 for calling the Pretrain obj's underlying member method code with example usage: obj.read_pretrain() and returns: vocab,  emb