code: def inference(self, g, device, batch_size, use_uva):
      g.ndata["h"] = g.ndata["feat"]
      sampler = MultiLayerFullNeighborSampler(
          1, prefetch_node_feats=["h"]
      )
      for l, layer in enumerate(self.layers):
          dataloader = DataLoader(
              g,
              torch.arange(g.num_nodes(), device=device),
              sampler,
              device=device,
              batch_size=batch_size,
              shuffle=False,
              drop_last=False,
              num_workers=0,
              use_ddp=True,
              use_uva=use_uva,
          )
          # in order to prevent running out of GPU memory, allocate a
          # shared output tensor 'y' in host memory
          y = shared_tensor(
              (
                  g.num_nodes(),
                  self.hid_size
                  if l != len(self.layers) - 1
                  else self.out_size,
              )
          )
          for input_nodes, output_nodes, blocks in (
              tqdm.tqdm(dataloader)
              if dist.get_rank() == 0
              else dataloader
          ):
              x = blocks[0].srcdata["h"]
              h = layer(blocks[0], x)  # len(blocks) = 1
              if l != len(self.layers) - 1:
                  h = F.relu(h)
                  h = self.dropout(h)
              # non_blocking (with pinned memory) to accelerate data transfer
              y[output_nodes] = h.to(y.device, non_blocking=True)
          # make sure all GPUs are done writing to 'y'
          dist.barrier()
          g.ndata["h"] = y if use_uva else y.to(device)
  
      g.ndata.pop("h")
      return y
  
imports: import argparse
  import os
  import time
  import dgl.function
  import dgl.nn
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import AsNodePredDataset, RedditDataset
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from dgl.multiprocessing import shared_tensor
  from ogb.nodeproppred import DglNodePropPredDataset
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 3 for calling the SAGE obj's underlying member method code with example usage: obj.inference(g, device, batch_size, use_uva) and returns: y