code: def __init__(self, g, k, neg_share=False, device=None):
      if device is None:
          device = g.device
      self.weights = g.in_degrees().float().to(device) ** 0.75
      self.k = k
      self.neg_share = neg_share
  
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 1 to create a new NegativeSampler object from inherited base classes: object with example: obj = NegativeSampler(g, k, neg_share, device)