code: def forward(self, blocks, x):
      hidden_x = x
      for layer_idx, (layer, block) in enumerate(
          zip(self.layers, blocks)
      ):
          hidden_x = layer(block, hidden_x)
          is_last_layer = layer_idx == len(self.layers) - 1
          if not is_last_layer:
              hidden_x = F.relu(hidden_x)
      return hidden_x
  
file_path: dgl/examples/sampling/graphbolt/quickstart/link_prediction.py
imports: import dgl.graphbolt
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import SAGEConv
  from torcheval.metrics import BinaryAUROC
label_desc: Use image node_id 2 for calling the GraphSAGE obj's underlying member method code with example usage: obj.forward(blocks, x) and returns: hidden_x