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 __init__(
      self,
      in_channels: int,
      out_channels: int,
      aggr: str = "mean",
      normalize: bool = False,
      root_weight: bool = True,
      project: bool = False,
      bias: bool = True,
  ):
      super().__init__()
  
      if aggr not in ["mean", "sum", "min", "max"]:
          raise ValueError(
              f"Aggregation function must be either 'mean', "
              f"'sum', 'min' or 'max' (got '{aggr}')"
          )
  
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.aggr = aggr
      self.normalize = normalize
      self.root_weight = root_weight
      self.project = project
  
      if self.project:
          self.pre_lin = Linear(in_channels, in_channels, bias=True)
  
      if self.root_weight:
          self.lin = Linear(2 * in_channels, out_channels, bias=bias)
      else:
          self.lin = Linear(in_channels, out_channels, bias=bias)
  
      self.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 1 to create a new CuGraphSAGEConv object from inherited base classes: CuGraphModule with example: obj = CuGraphSAGEConv(in_channels, out_channels, aggr, normalize, root_weight, project, bias)