class_docstr: Sampler for validation and testing
  
  Parameters
  ----------
  g : DGLGraph
      Graph containing KG graph
  edges : tensor
      Seed edges
  batch_size : int
      Batch size of each mini batch.
  neg_sample_size : int
      How many negative edges sampled for each node.
  neg_chunk_size : int
      How many edges in one chunk. We split one batch into chunks.
  mode : str
      Sampling mode.
  number_workers: int
      Number of workers used in parallel for this sampler
  filter_false_neg : bool
      If True, exlucde true positive edges in sampled negative edges
      If False, return all sampled negative edges even there are positive edges
      Default: True
class_docstr_tok: ["Sampler","for","validation","and","testing","Parameters","--","--","--","--","--","g",":","DGLGraph","Graph","containing","KG","graph","edges",":","tensor","Seed","edges","batch_size",":","int","Batch","size","of","each","mini","batch",".","neg_sample_size",":","int","How","many","negative","edges","sampled","for","each","node",".","neg_chunk_size",":","int","How","many","edges","in","one","chunk",".","We","split","one","batch","into","chunks",".","mode",":","str","Sampling","mode",".","number_workers",":","int","Number","of","workers","used","in","parallel","for","this","sampler","filter_false_neg",":","bool","If","True",",","exlucde","true","positive","edges","in","sampled","negative","edges","If","False",",","return","all","sampled","negative","edges","even","there","are","positive","edges","Default",":","True"]
code: def __iter__(self):
      return self
  
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 2 for calling the EvalSampler obj's underlying member method code with example usage: obj.__iter__() and returns: self