code: def forward(self, nodes_output):
      aggregated_features = []
      for l in range(nodes_output.shape[0]):
          output_node_features = nodes_output[l, :, :]
          weights = self.Wq(output_node_features)
          aggregated_features.append(weights)
      aggregated_features = torch.cat(aggregated_features, dim=0)
      print(aggregated_features.shape)
      return aggregated_features
  
imports: import torch
  import numpy
  import torch.nn
  from typing import List
  import torch.nn.functional
  from torch_geometric.nn import GCNConv
label_desc: Use image node_id 2 for calling the AggregateTemporalNodeFeatures obj's underlying member method code with example usage: obj.forward(nodes_output) and returns: aggregated_features