code: def forward(self, words):
      word_subinds = np.empty([0], dtype=np.int64)
      word_offsets = [0]
      for word in words:
          _, subinds = self.model.get_subwords(word)
          word_subinds = np.concatenate((word_subinds, subinds))
          word_offsets.append(word_offsets[-1] + len(subinds))
      word_offsets = word_offsets[:-1]
      ind = Variable(torch.LongTensor(word_subinds))
      offsets = Variable(torch.LongTensor(word_offsets))
      return super().forward(ind, offsets)
  
file_path: fastText/python/doc/examples/FastTextEmbeddingBag.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from torch.nn.modules.sparse import EmbeddingBag
  import numpy
  import torch
  import random
  import string
  import time
  from fasttext import load_model
  from torch.autograd import Variable
label_desc: Use image node_id 2 for calling the FastTextEmbeddingBag obj's underlying member method code with example usage: obj.forward(words) and returns: super