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 reset_parameters(self):
      gain = nn.init.calculate_gain("relu")
      nn.init.xavier_uniform_(self.fc_self.weight, gain=gain)
      nn.init.xavier_uniform_(self.fc_neigh.weight, gain=gain)
  
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 2 for calling the SAGEConv obj's underlying member method code with example usage: obj.reset_parameters() without return types