code: def forward(self, sampled_matrices, x):
      hidden_x = x
      for layer_idx, (layer, sampled_matrix) in enumerate(
          zip(self.layers, sampled_matrices)
      ):
          hidden_x = layer(sampled_matrix, hidden_x)
          if layer_idx != len(self.layers) - 1:
              hidden_x = F.relu(hidden_x)
              hidden_x = self.dropout(hidden_x)
      return hidden_x
  
imports: import argparse
  from functools import partial
  import dgl.graphbolt
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from dgl.graphbolt.subgraph_sampler import SubgraphSampler
  from torch.utils.data import functional_datapipe
label_desc: Use image node_id 2 for calling the SAGE obj's underlying member method code with example usage: obj.forward(sampled_matrices, x) and returns: hidden_x