class_docstr: Negative sampling based skip-gram
class_docstr_tok: ["Negative","sampling","based","skip-gram"]
docstr: do fast sigmoid by looking up in a pre-defined table
docstr_tok: ["do","fast","sigmoid","by","looking","up","in","a","pre-defined","table"]
code: def fast_sigmoid(self, score):
      """do fast sigmoid by looking up in a pre-defined table"""
      idx = torch.floor((score + 6.01) / 0.01).long()
      return self.lookup_table[idx]
  
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 7 for calling the SkipGramModel obj's underlying member method code with example usage: obj.fast_sigmoid(score) and returns: self