class_docstr: Sparse Embedding for Knowledge Graph
  It is used to store both entity embeddings and relation embeddings.
  
  Parameters
  ----------
  args :
      Global configs.
  num : int
      Number of embeddings.
  dim : int
      Embedding dimention size.
  device : th.device
      Device to store the embedding.
docstr: Initializing the embeddings.
  
  Parameters
  ----------
  emb_init : float
      The intial embedding range should be [-emb_init, emb_init].
docstr_tok: ["Initializing","the","embeddings",".","Parameters","--","--","--","--","--","emb_init",":","float","The","intial","embedding","range","should","be","[","-emb_init",",","emb_init","]","."]
code: def init(self, emb_init):
      """Initializing the embeddings.
  
      Parameters
      ----------
      emb_init : float
          The intial embedding range should be [-emb_init, emb_init].
      """
      INIT.uniform_(self.emb, -emb_init, emb_init)
      INIT.zeros_(self.state_sum)
  
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
  import torch.multiprocessing
  from torch.multiprocessing import Queue
  from _thread import start_new_thread
  import traceback
  from functools import wraps
  from ..None import *
label_desc: Use image node_id 2 for calling the ExternalEmbedding obj's underlying member method code with example usage: obj.init(emb_init) without return types