class_docstr: Collects rooted random-walk based subgraphs for each node in the graph,
  as described in the `"From Stars to Subgraphs: Uplifting Any GNN with Local
  Structure Awareness" <https://arxiv.org/abs/2110.03753>`_ paper.
  
  Args:
      walk_length (int): the length of the random walk.
      repeat (int, optional): The number of times of repeating the random
          walk to reduce randomness. (default: :obj:`1`)
class_docstr_tok: ["Collects","rooted","random-walk","based","subgraphs","for","each","node","in","the","graph",",","as","described","in","the","`","``","From","Stars","to","Subgraphs",":","Uplifting","Any","GNN","with","Local","Structure","Awareness","''","<","https",":","\/\/arxiv.org\/abs\/2110.03753",">","`","_","paper",".","Args",":","walk_length","(","int",")",":","the","length","of","the","random","walk",".","repeat","(","int",",","optional",")",":","The","number","of","times","of","repeating","the","random","walk","to","reduce","randomness",".","(","default",":",":","obj",":","`","1","`",")"]
code: def __init__(self, walk_length: int, repeat: int = 1):
      super().__init__()
      self.walk_length = walk_length
      self.repeat = repeat
  
imports: import copy
  from abc import ABC, abstractmethod
  from typing import Any, Tuple
  import torch
  from torch import Tensor
  from torch_geometric.data import Data
  from torch_geometric.transforms import BaseTransform
  from torch_geometric.utils import to_torch_csc_tensor
label_desc: Use image node_id 1 to create a new RootedRWSubgraph object from inherited base classes: RootedSubgraph with example: obj = RootedRWSubgraph(walk_length, repeat)