class_docstr: A loader and saver for pretrained embeddings. 
docstr: Open a vector file using the provided function and read from it.
docstr_tok: ["Open","a","vector","file","using","the","provided","function","and","read","from","it","."]
code: def read_from_file(filename, max_vocab=None):
      """
      Open a vector file using the provided function and read from it.
      """
      logger.info("Reading pretrained vectors from %s ...", filename)
  
      # some vector files, such as Google News, use tabs
      tab_space_pattern = re.compile(r"[ \t]+")
      first = True
      cols = None
      lines = []
      failed = 0
      unk_line = None
      with open_read_binary(filename) as f:
          for i, line in enumerate(f):
              try:
                  line = line.decode()
              except UnicodeDecodeError:
                  failed += 1
                  continue
              line = line.rstrip()
              if not line:
                  continue
              pieces = tab_space_pattern.split(line)
              if first:
                  # the first line contains the number of word vectors and the dimensionality
                  # note that a 1d embedding with a number as the first entry
                  # will fail to read properly.  we ignore that case
                  first = False
                  if len(pieces) == 2:
                      cols = int(pieces[1])
                      continue
  
              if pieces[0] == "<unk>":
                  if unk_line is not None:
                      logger.error(
                          "More than one <unk> line in the pretrain!  Keeping the most recent one"
                      )
                  else:
                      logger.debug(
                          "Found an unk line while reading the pretrain"
                      )
                  unk_line = pieces
              else:
                  if (
                      not max_vocab
                      or max_vocab < 0
                      or len(lines) < max_vocab
                  ):
                      lines.append(pieces)
  
      if cols is None:
          # another failure case: all words have spaces in them
          cols = min(len(x) for x in lines) - 1
      rows = len(lines)
      emb = np.zeros((rows + len(VOCAB_PREFIX), cols), dtype=np.float32)
      if unk_line is not None:
          emb[UNK_ID] = [float(x) for x in unk_line[-cols:]]
      for i, line in enumerate(lines):
          emb[i + len(VOCAB_PREFIX)] = [float(x) for x in line[-cols:]]
  
      # if there were word pieces separated with spaces, rejoin them with nbsp instead
      # this way, the normalize_unit method in vocab.py can find the word at test time
      words = ["\xa0".join(line[:-cols]) for line in lines]
      if failed > 0:
          logger.info("Failed to read %d lines from embedding", failed)
      return words, emb, failed
  
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 9 for calling the Pretrain obj's underlying member method code with example usage: obj.read_from_file(filename, max_vocab) and returns: words,  emb,  failed