class_docstr: An accelerated GraphSAGE layer from `Inductive Representation Learning
  on Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`, with the sparse
  aggregation accelerated by cugraph-ops.
  
  Parameters
  ----------
  in_feats : int or tuple
      Input feature size. If a scalar is given, the source and destination
      nodes are required to be the same.
  out_feats : int
      Output feature size.
  aggregator_type : str
      Aggregator type to use ("mean", "sum", "min", "max", "pool", "gcn").
  feat_drop : float
      Dropout rate on features, default: ``0``.
  bias : bool
      If True, adds a learnable bias to the output. Default: ``True``.
  
  Examples
  --------
  >>> import dgl
  >>> import torch
  >>> from cugraph_dgl.nn import SAGEConv
  ...
  >>> device = 'cuda'
  >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])).to(device)
  >>> g = dgl.add_self_loop(g)
  >>> feat = torch.ones(6, 10).to(device)
  >>> conv = SAGEConv(10, 2, 'mean').to(device)
  >>> res = conv(g, feat)
  >>> res
  tensor([[-1.1690,  0.1952],
          [-1.1690,  0.1952],
          [-1.1690,  0.1952],
          [-1.1690,  0.1952],
          [-1.1690,  0.1952],
          [-1.1690,  0.1952]], device='cuda:0', grad_fn=<AddmmBackward0>)
docstr: Reinitialize learnable parameters.
docstr_tok: ["Reinitialize","learnable","parameters","."]
code: def reset_parameters(self):
      r"""Reinitialize learnable parameters."""
      self.lin.reset_parameters()
      if self.pre_lin is not None:
          self.pre_lin.reset_parameters()
  
imports: from typing import Optional, Tuple, Union
  from cugraph_dgl.nn.conv.base import BaseConv, SparseGraph
  from cugraph.utilities.utils import import_optional
label_desc: Use image node_id 2 for calling the SAGEConv obj's underlying member method code with example usage: obj.reset_parameters() without return types