class_docstr: The GraphSAGE operator from the `"Inductive Representation Learning on
  Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper.
  
  :class:`CuGraphSAGEConv` is an optimized version of
  :class:`~torch_geometric.nn.conv.SAGEConv` based on the :obj:`cugraph-ops`
  package that fuses message passing computation for accelerated execution
  and lower memory footprint.
class_docstr_tok: ["The","GraphSAGE","operator","from","the","`","``","Inductive","Representation","Learning","on","Large","Graphs","''","<","https",":","\/\/arxiv.org\/abs\/1706.02216",">","`","_","paper",".",":","class",":","`","CuGraphSAGEConv","`","is","an","optimized","version","of",":","class",":","`","~torch_geometric.nn.conv.SAGEConv","`","based","on","the",":","obj",":","`","cugraph-ops","`","package","that","fuses","message","passing","computation","for","accelerated","execution","and","lower","memory","footprint","."]
code: def reset_parameters(self):
      if self.project:
          self.pre_lin.reset_parameters()
      self.lin.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/conv/cugraph/sage_conv.py
imports: from typing import Optional, Tuple
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Linear
  from torch_geometric.nn.conv.cugraph import CuGraphModule
  from torch_geometric.nn.conv.cugraph.base import LEGACY_MODE
label_desc: Use image node_id 2 for calling the CuGraphSAGEConv obj's underlying member method code with example usage: obj.reset_parameters() without return types