class_docstr: Collects rooted :math:`k`-hop EgoNets 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:
      num_hops (int): the number of hops :math:`k`.
class_docstr_tok: ["Collects","rooted",":","math",":","`","k","`","-hop","EgoNets","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",":","num_hops","(","int",")",":","the","number","of","hops",":","math",":","`","k","`","."]
code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}(num_hops={self.num_hops})"
  
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 3 for calling the RootedEgoNets obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str