class_docstr: Dataset for validation or testing
  
  Parameters
  ----------
  dataset : KGDataset
      Original dataset.
  args :
      Global configs.
class_docstr_tok: ["Dataset","for","validation","or","testing","Parameters","--","--","--","--","--","dataset",":","KGDataset","Original","dataset",".","args",":","Global","configs","."]
docstr: Get all edges in this dataset
  
  Parameters
  ----------
  eval_type : str
      Sampling type, 'valid' for validation and 'test' for testing
  
  Returns
  -------
  np.array
      Edges
docstr_tok: ["Get","all","edges","in","this","dataset","Parameters","--","--","--","--","--","eval_type",":","str","Sampling","type",",","'valid","'","for","validation","and","'test","'","for","testing","Returns","--","--","--","-","np.array","Edges"]
code: def get_dicts(self, eval_type):
      """Get all edges in this dataset
  
      Parameters
      ----------
      eval_type : str
          Sampling type, 'valid' for validation and 'test' for testing
  
      Returns
      -------
      np.array
          Edges
      """
      if eval_type == "valid":
          return self.valid_dict
      elif eval_type == "test":
          return self.test_dict
      else:
          raise Exception("get invalid type: " + eval_type)
  
imports: import math
  import numpy
  import scipy
  import dgl.backend
  import dgl
  import os
  import sys
  import pickle
  import time
  from dgl.base import NID, EID
  import pdb
label_desc: Use image node_id 3 for calling the EvalDataset obj's underlying member method code with example usage: obj.get_dicts(eval_type) and returns: self, self