code: def forward(self, blocks, x):
      h = x
      for l, (layer, block) in enumerate(zip(self.layers, blocks)):
          h = layer(
              block,
              h,
              edge_weight=block.edata["edge_weights"]
              if "edge_weights" in block.edata
              else None,
          )
          if l != len(self.layers) - 1:
              h = self.activation(h)
              h = self.dropout(h)
      return h
  
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 3 for calling the SAGE obj's underlying member method code with example usage: obj.forward(blocks, x) and returns: h