class_docstr: Negative sampling based skip-gram
class_docstr_tok: ["Negative","sampling","based","skip-gram"]
docstr: Write embedding to local file. Only used when node ids are numbers.
  
  Parameter
  ---------
  dataset DeepwalkDataset : the dataset
  file_name str : the file name
docstr_tok: ["Write","embedding","to","local","file",".","Only","used","when","node","ids","are","numbers",".","Parameter","--","--","--","--","-","dataset","DeepwalkDataset",":","the","dataset","file_name","str",":","the","file","name"]
code: def save_embedding(self, dataset, file_name):
      """Write embedding to local file. Only used when node ids are numbers.
  
      Parameter
      ---------
      dataset DeepwalkDataset : the dataset
      file_name str : the file name
      """
      embedding = self.u_embeddings.weight.cpu().data.numpy()
      if self.norm:
          embedding /= np.sqrt(
              np.sum(embedding * embedding, 1)
          ).reshape(-1, 1)
      np.save(file_name, embedding)
  
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 11 for calling the SkipGramModel obj's underlying member method code with example usage: obj.save_embedding(dataset, file_name) without return types