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.
  ctx : mx.ctx
      Device context to store the embedding.
class_docstr_tok: ["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",".","ctx",":","mx.ctx","Device","context","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].
      """
      nd.random.uniform(
          -emb_init,
          emb_init,
          shape=self.emb.shape,
          dtype=self.emb.dtype,
          ctx=self.emb.context,
          out=self.emb,
      )
  
imports: import os
  import numpy
  import mxnet
  from mxnet import gluon
  from mxnet import ndarray
  from .score_fun import *
  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