class_docstr: Negative sampling based skip-gram
class_docstr_tok: ["Negative","sampling","based","skip-gram"]
docstr: share the parameters across subprocesses
docstr_tok: ["share","the","parameters","across","subprocesses"]
code: def share_memory(self):
      """share the parameters across subprocesses"""
      if self.fst:
          self.fst_u_embeddings.weight.share_memory_()
          self.fst_state_sum_u.share_memory_()
      if self.snd:
          self.snd_u_embeddings.weight.share_memory_()
          self.snd_v_embeddings.weight.share_memory_()
          self.snd_state_sum_u.share_memory_()
          self.snd_state_sum_v.share_memory_()
  
imports: import random
  import numpy
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  from torch.multiprocessing import Queue
  from torch.nn import init
label_desc: Use image node_id 4 for calling the SkipGramModel obj's underlying member method code with example usage: obj.share_memory() without return types