code: def __init__(self, nodes, mode="train", repeat=1):
      self.repeat = repeat
      if (
          int(paddle.distributed.get_world_size()) == 1
          or mode != "train"
      ):
          self.data = nodes
      else:
          self.data = nodes[
              int(paddle.distributed.get_rank()) :: int(
                  paddle.distributed.get_world_size()
              )
          ]
  
file_path: PGL/examples/graph_based_clustering_demo/dataset.py
imports: import os
  import paddle
  import numpy
  from pgl import graph_kernel
  from pgl.utils.logger import log
  from pgl.utils.data import Dataset
  from pgl.sampling import node2vec_walk
  from pgl.graph_kernel import skip_gram_gen_pair
label_desc: Use image node_id 1 to create a new ShardedDataset object from inherited base classes: Dataset with example: obj = ShardedDataset(nodes, mode, repeat)