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 __init__(
      self,
      in_size,
      out_size,
  ):
      super(SAGEConv, self).__init__()
      self._in_src_feats, self._in_dst_feats = in_size, in_size
      self._out_size = out_size
  
      self.fc_neigh = nn.Linear(
          self._in_src_feats, out_size, bias=False
      )
      self.fc_self = nn.Linear(self._in_dst_feats, out_size, bias=True)
      self.reset_parameters()
  
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 1 to create a new SAGEConv object from inherited base classes: nn with example: obj = SAGEConv(in_size, out_size)