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 _collate(self, batch):
      batch_graphs, batch_labels = map(list, zip(*batch))
  
      batch_graphs = dgl.batch(batch_graphs)
      batch_labels = torch.stack(batch_labels)
      return batch_graphs, batch_labels
  
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 3 for calling the SEALSampler obj's underlying member method code with example usage: obj._collate(batch) and returns: batch_graphs,  batch_labels