class_docstr: Sampler for SEAL in paper(no-block version)
  The  strategy is to sample all the k-hop neighbors around the two target nodes.
  Attributes:
      graph(DGLGraph): The graph
      hop(int): num of hop
      num_workers(int): num of workers
class_docstr_tok: ["Sampler","for","SEAL","in","paper","(","no-block","version",")","The","strategy","is","to","sample","all","the","k-hop","neighbors","around","the","two","target","nodes",".","Attributes",":","graph","(","DGLGraph",")",":","The","graph","hop","(","int",")",":","num","of","hop","num_workers","(","int",")",":","num","of","workers"]
code: def __call__(self, edges, labels):
      subgraph_list = []
      labels_list = []
      edge_dataset = EdgeDataSet(
          edges, labels, transform=self.sample_subgraph
      )
      self.print_fn(
          "Using {} workers in sampling job.".format(self.num_workers)
      )
      sampler = DataLoader(
          edge_dataset,
          batch_size=32,
          num_workers=self.num_workers,
          shuffle=False,
          collate_fn=self._collate,
      )
      for subgraph, label in tqdm(sampler, ncols=100):
          label_copy = deepcopy(label)
          subgraph = dgl.unbatch(subgraph)
  
          del label
          subgraph_list += subgraph
          labels_list.append(label_copy)
  
      return subgraph_list, torch.cat(labels_list)
  
imports: import os.path
  from copy import deepcopy
  import dgl
  import torch
  from dgl import add_self_loop, DGLGraph, NID
  from dgl.dataloading.negative_sampler import Uniform
  from torch.utils.data import DataLoader, Dataset
  from tqdm import tqdm
  from utils import drnl_node_labeling
label_desc: Use image node_id 4 for calling the SEALSampler obj's underlying member method code with example usage: obj.__call__(edges, labels) and returns: subgraph_list,  torch