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 __init__(self, edges, batch_size, mode):
      self.edges = edges
      self.batch_size = batch_size
      self.mode = mode
      self.neg_head = "head" in mode
      self.cnt = 0
      if "head" in self.mode:
          self.mode = "t,r->h"
          self.num_edges = len(self.edges["t,r->h"]["tr"])
      elif "tail" in self.mode:
          self.mode = "h,r->t"
          self.num_edges = len(self.edges["h,r->t"]["hr"])
  
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 1 to create a new WikiEvalSampler object from inherited base classes: object with example: obj = WikiEvalSampler(edges, batch_size, mode)