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: Return embeddings in trace.
          
docstr_tok: ["Return","embeddings","in","trace","."]
code: def curr_emb(self):
      """Return embeddings in trace."""
      data = [data for _, data in self.trace]
      return nd.concat(*data, dim=0)
  
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 6 for calling the ExternalEmbedding obj's underlying member method code with example usage: obj.curr_emb() and returns: nd