docstr: Arguments:
  name: name of the file that contains the vectors
  cache: directory for cached vectors
  url: url for download if vectors not found in cache
  unk_init (callback): by default, initalize out-of-vocabulary word vectors
      to zero vectors; can be any function that takes in a Tensor and
      returns a Tensor of the same size
docstr_tok: ["Arguments",":","name",":","name","of","the","file","that","contains","the","vectors","cache",":","directory","for","cached","vectors","url",":","url","for","download","if","vectors","not","found","in","cache","unk_init","(","callback",")",":","by","default",",","initalize","out-of-vocabulary","word","vectors","to","zero","vectors",";","can","be","any","function","that","takes","in","a","Tensor","and","returns","a","Tensor","of","the","same","size"]
code: def __init__(
      self,
      name,
      cache=".vector_cache",
      url=None,
      unk_init=torch.Tensor.zero_,
  ):
      """Arguments:
      name: name of the file that contains the vectors
      cache: directory for cached vectors
      url: url for download if vectors not found in cache
      unk_init (callback): by default, initalize out-of-vocabulary word vectors
          to zero vectors; can be any function that takes in a Tensor and
          returns a Tensor of the same size
      """
      self.unk_init = unk_init
      self.cache(name, cache, url=url)
  
imports: from __future__ import unicode_literals
  import array
  from collections import defaultdict
  import io
  import logging
  import os
  import zipfile
  import six
  from six.moves.urllib.request import urlretrieve
  import torch
  from tqdm import tqdm
  import tarfile
  from .utils import reporthook
label_desc: Use image node_id 1 to create a new Vectors object from inherited base classes: object with example: obj = Vectors(name, cache, url, unk_init)