class_docstr: An abstract base class for implementing custom edge connection
  operators as described in the `"Understanding Pooling in Graph Neural
  Networks" <https://arxiv.org/abs/1905.05178>`_ paper.
  
  Specifically, :class:`Connect` determines for each pair of supernodes the
  presence or abscene of an edge based on the existing edges between the
  nodes in the two supernodes.
  The operator also computes pooled edge features and batch vectors
  (if present).
class_docstr_tok: ["An","abstract","base","class","for","implementing","custom","edge","connection","operators","as","described","in","the","`","``","Understanding","Pooling","in","Graph","Neural","Networks","''","<","https",":","\/\/arxiv.org\/abs\/1905.05178",">","`","_","paper",".","Specifically",",",":","class",":","`","Connect","`","determines","for","each","pair","of","supernodes","the","presence","or","abscene","of","an","edge","based","on","the","existing","edges","between","the","nodes","in","the","two","supernodes",".","The","operator","also","computes","pooled","edge","features","and","batch","vectors","(","if","present",")","."]
docstr: Resets all learnable parameters of the module.
docstr_tok: ["Resets","all","learnable","parameters","of","the","module","."]
code: def reset_parameters(self):
      r"""Resets all learnable parameters of the module."""
      pass
  
file_path: pytorch_geometric/torch_geometric/nn/pool/connect/base.py
imports: from dataclasses import dataclass
  from typing import Optional
  import torch
  from torch import Tensor
  import torch_geometric.typing
  from torch_geometric.nn.pool.select import SelectOutput
label_desc: Use image node_id 1 for calling the Connect obj's underlying member method code with example usage: obj.reset_parameters() without return types