class_docstr: The relational graph convolutional operator from the `"Modeling
  Relational Data with Graph Convolutional Networks"
  <https://arxiv.org/abs/1703.06103>`_ paper.
  
  :class:`CuGraphRGCNConv` is an optimized version of
  :class:`~torch_geometric.nn.conv.RGCNConv` based on the :obj:`cugraph-ops`
  package that fuses message passing computation for accelerated execution
  and lower memory footprint.
class_docstr_tok: ["The","relational","graph","convolutional","operator","from","the","`","``","Modeling","Relational","Data","with","Graph","Convolutional","Networks","''","<","https",":","\/\/arxiv.org\/abs\/1703.06103",">","`","_","paper",".",":","class",":","`","CuGraphRGCNConv","`","is","an","optimized","version","of",":","class",":","`","~torch_geometric.nn.conv.RGCNConv","`","based","on","the",":","obj",":","`","cugraph-ops","`","package","that","fuses","message","passing","computation","for","accelerated","execution","and","lower","memory","footprint","."]
code: def reset_parameters(self):
      end = -1 if self.root_weight else None
      glorot(self.weight[:end])
      glorot(self.comp)
      if self.root_weight:
          glorot(self.weight[-1])
      zeros(self.bias)
  
file_path: pytorch_geometric/torch_geometric/nn/conv/cugraph/rgcn_conv.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch.nn import Parameter
  from torch_geometric.nn.conv.cugraph import CuGraphModule
  from torch_geometric.nn.conv.cugraph.base import LEGACY_MODE
  from torch_geometric.nn.inits import glorot, zeros
label_desc: Use image node_id 2 for calling the CuGraphRGCNConv obj's underlying member method code with example usage: obj.reset_parameters() without return types