class_docstr: Negative sampling based skip-gram
class_docstr_tok: ["Negative","sampling","based","skip-gram"]
docstr: For ogb leaderboard.
docstr_tok: ["For","ogb","leaderboard","."]
code: def save_embedding_pt(self, dataset, file_name):
      """For ogb leaderboard."""
      embedding = torch.Tensor(self.get_embedding()).cpu()
      embedding_empty = torch.zeros_like(embedding.data)
      valid_nodes = torch.LongTensor(dataset.valid_nodes)
      valid_embedding = embedding.data.index_select(0, valid_nodes)
      embedding_empty.index_add_(0, valid_nodes, valid_embedding)
  
      torch.save(embedding_empty, file_name)
  
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 14 for calling the SkipGramModel obj's underlying member method code with example usage: obj.save_embedding_pt(dataset, file_name) without return types