class_docstr: GraphSAGE layer from `Inductive Representation Learning on
  Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__
class_docstr_tok: ["GraphSAGE","layer","from","`","Inductive","Representation","Learning","on","Large","Graphs","<","https",":","\/\/arxiv.org\/pdf\/1706.02216.pdf",">","`","__"]
code: def __init__(
      self,
      in_feats,
      out_feats,
  ):
      super(SAGEConv, self).__init__()
      self._in_src_feats, self._in_dst_feats = in_feats, in_feats
      self._out_feats = out_feats
  
      self.fc_neigh = nn.Linear(
          self._in_src_feats, out_feats, bias=False
      )
      self.fc_self = nn.Linear(self._in_dst_feats, out_feats, bias=True)
      self.reset_parameters()
  
imports: import argparse
  from functools import partial
  import dgl.graphbolt
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from dgl.graphbolt.subgraph_sampler import SubgraphSampler
  from torch.utils.data import functional_datapipe
label_desc: Use image node_id 1 to create a new SAGEConv object from inherited base classes: nn with example: obj = SAGEConv(in_feats, out_feats)