class_docstr: Word node embedding application.
  
  Given a corpus, it embeds each word into a continuous vector representation.
  The learned embeddings can be used for natural language processing tasks.
  This can be viewed as a variant of the word2vec algorithm, with random walk augmentation support.
  The similarity between node embeddings can be measured by cosine distance.
  
  Supported Models:
      - LINE (`LINE: Large-scale Information Network Embedding`_)
  
  Parameters:
      dim (int): dimension of embeddings
      gpus (list of int, optional): GPU ids, default is all GPUs
      cpu_per_gpu (int, optional): number of CPU threads per GPU, default is all CPUs
      float_type (dtype, optional): type of parameters
      index_type (dtype, optional): type of graph indexes
  
  See also:
      :class:`WordGraph <graphvite.graph.WordGraph>`,
      :class:`GraphSolver <graphvite.solver.GraphSolver>`
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code: def set_parameters(self, model):
      mapping = self.get_mapping(
          self.graph.id2name, model.graph.name2id
      )
      self.solver.vertex_embeddings[:] = model.solver.vertex_embeddings[
          mapping
      ]
      self.solver.context_embeddings[
          :
      ] = model.solver.context_embeddings[mapping]
  
imports: from __future__ import print_function, absolute_import, unicode_literals, division
  import os
  import re
  import pickle
  import logging
  import multiprocessing
  from collections import defaultdict
  from future.builtins import str, map, range
  from easydict import EasyDict
  import numpy
  from ..None import lib, cfg, auto
  from ..None import graph, solver
  from ..util import assert_in, monitor, SharedNDArray
label_desc: Use image node_id 3 for calling the WordGraphApplication obj's underlying member method code with example usage: obj.set_parameters(model) without return types