code: def inference(self, g, device, batch_size, use_uva, num_workers):
      # The difference between this inference function and the one in the official
      # example is that the intermediate results can also benefit from prefetching.
      g.ndata["h"] = g.ndata["features"]
      sampler = dgl.dataloading.MultiLayerFullNeighborSampler(
          1, prefetch_node_feats=["h"]
      )
      pin_memory = g.device != device and use_uva
      dataloader = dgl.dataloading.DataLoader(
          g,
          th.arange(g.num_nodes(), dtype=g.idtype, device=g.device),
          sampler,
          device=device,
          batch_size=batch_size,
          shuffle=False,
          drop_last=False,
          use_uva=use_uva,
          num_workers=num_workers,
          persistent_workers=(num_workers > 0),
      )
  
      self.eval()
  
      for l, layer in enumerate(self.layers):
          y = th.empty(
              g.num_nodes(),
              self.n_hidden
              if l != len(self.layers) - 1
              else self.n_classes,
              dtype=g.ndata["h"].dtype,
              device=g.device,
              pin_memory=pin_memory,
          )
          for input_nodes, output_nodes, blocks in tqdm.tqdm(
              dataloader
          ):
              x = blocks[0].srcdata["h"]
              h = layer(blocks[0], x)
              if l < len(self.layers) - 1:
                  h = self.activation(h)
                  h = self.dropout(h)
              # by design, our output nodes are contiguous
              y[
                  output_nodes[0].item() : output_nodes[-1].item() + 1
              ] = h.to(y.device)
          g.ndata["h"] = y
      return y
  
file_path: dgl/examples/pytorch/labor/model.py
imports: import dgl
  import dgl.nn
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.functional
  import torch.nn
  import tqdm
label_desc: Use image node_id 4 for calling the SAGE obj's underlying member method code with example usage: obj.inference(g, device, batch_size, use_uva, num_workers) and returns: y