class_docstr: The graph attentional operator from the `"Graph Attention Networks"
  <https://arxiv.org/abs/1710.10903>`_ paper.
  
  :class:`CuGraphGATConv` is an optimized version of
  :class:`~torch_geometric.nn.conv.GATConv` based on the :obj:`cugraph-ops`
  package that fuses message passing computation for accelerated execution
  and lower memory footprint.
class_docstr_tok: ["The","graph","attentional","operator","from","the","`","``","Graph","Attention","Networks","''","<","https",":","\/\/arxiv.org\/abs\/1710.10903",">","`","_","paper",".",":","class",":","`","CuGraphGATConv","`","is","an","optimized","version","of",":","class",":","`","~torch_geometric.nn.conv.GATConv","`","based","on","the",":","obj",":","`","cugraph-ops","`","package","that","fuses","message","passing","computation","for","accelerated","execution","and","lower","memory","footprint","."]
code: def __repr__(self) -> str:
      return (
          f"{self.__class__.__name__}({self.in_channels}, "
          f"{self.out_channels}, heads={self.heads})"
      )
  
file_path: pytorch_geometric/torch_geometric/nn/conv/cugraph/gat_conv.py
imports: from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from torch.nn import Linear, 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 zeros
label_desc: Use image node_id 4 for calling the CuGraphGATConv obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str, self, str