code: def __call__(self, g, eids):
      src, _ = g.find_edges(eids)
      n = len(src)
      if self.neg_share and n % self.k == 0:
          dst = self.weights.multinomial(n, replacement=True)
          dst = dst.view(-1, 1, self.k).expand(-1, self.k, -1).flatten()
      else:
          dst = self.weights.multinomial(n * self.k, replacement=True)
      src = src.repeat_interleave(self.k)
      return src, dst
  
imports: import argparse
  import os
  import time
  import dgl.function
  import dgl.nn
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import AsNodePredDataset, RedditDataset
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from dgl.multiprocessing import shared_tensor
  from ogb.nodeproppred import DglNodePropPredDataset
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 2 for calling the NegativeSampler obj's underlying member method code with example usage: obj.__call__(g, eids) and returns: src,  dst