code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout,
      ngnn_type,
      dataset,
      reduce="mean",
  ):
      super(SAGE, self).__init__()
  
      self.dataset = dataset
      self.convs = torch.nn.ModuleList()
  
      num_nonl_layers = (
          1 if num_layers <= 2 else 2
      )  # number of nonlinear layers in each conv layer
      if ngnn_type == "input":
          self.convs.append(
              NGNN_SAGEConv(
                  in_channels,
                  hidden_channels,
                  hidden_channels,
                  num_nonl_layers,
                  reduce=reduce,
              )
          )
          for _ in range(num_layers - 2):
              self.convs.append(
                  SAGEConv(hidden_channels, hidden_channels, reduce)
              )
      elif ngnn_type == "hidden":
          self.convs.append(
              SAGEConv(in_channels, hidden_channels, reduce)
          )
          for _ in range(num_layers - 2):
              self.convs.append(
                  NGNN_SAGEConv(
                      hidden_channels,
                      hidden_channels,
                      hidden_channels,
                      num_nonl_layers,
                      reduce=reduce,
                  )
              )
  
      self.convs.append(SAGEConv(hidden_channels, out_channels, reduce))
  
      self.dropout = dropout
      self.reset_parameters()
  
imports: import argparse
  import math
  import dgl
  import torch
  import torch.nn.functional
  from dgl.dataloading.negative_sampler import GlobalUniform
  from dgl.nn.pytorch import GraphConv, SAGEConv
  from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
  from torch.nn import Linear
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new SAGE object from inherited base classes: torch.nn with example: obj = SAGE(in_channels, hidden_channels, out_channels, num_layers, dropout, ngnn_type, dataset, reduce)