class_docstr: A loader and saver for pretrained embeddings. 
docstr: Read vectors from CSV
  
  Skips the first row
docstr_tok: ["Read","vectors","from","CSV","Skips","the","first","row"]
code: def read_from_csv(filename):
      """
      Read vectors from CSV
  
      Skips the first row
      """
      logger.info(
          "Reading pretrained vectors from csv file %s ...", filename
      )
      with open_read_text(filename) as fin:
          csv_reader = csv.reader(fin)
          # the header of the thai csv vector file we have is just the number of columns
          # so we read past the first line
          for line in csv_reader:
              break
          lines = [line for line in csv_reader]
  
      rows = len(lines)
      cols = len(lines[0]) - 1
  
      emb = np.zeros((rows + len(VOCAB_PREFIX), cols), dtype=np.float32)
      for i, line in enumerate(lines):
          emb[i + len(VOCAB_PREFIX)] = [float(x) for x in line[-cols:]]
      words = [line[0].replace(" ", "\xa0") for line in lines]
      return words, 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 8 for calling the Pretrain obj's underlying member method code with example usage: obj.read_from_csv(filename) and returns: words,  emb