class_docstr: LADIES layer from `Layer-Dependent Importance Sampling
  for Training Deep and Large Graph Convolutional Networks
  <https://arxiv.org/abs/1911.07323.pdf>`__
class_docstr_tok: ["LADIES","layer","from","`","Layer-Dependent","Importance","Sampling","for","Training","Deep","and","Large","Graph","Convolutional","Networks","<","https",":","\/\/arxiv.org\/abs\/1911.07323.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
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from dgl.data import AsNodePredDataset
  from dgl.sparse import sp_broadcast_v
  from ogb.nodeproppred import DglNodePropPredDataset
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