code: def forward(self, graph):
      node_feat = graph.ndata["node_feat"]
  
      if self.gl_type == "node_emb_refined":
          new_graph = self.graph_learner.topology(
              node_feat, graph.init_adj, node_mask=None
          )
      else:
          new_graph = self.graph_learner.topology(
              node_feat, node_mask=None
          )
  
      # convert GraphData to DGLGraph
      dgl_graph = new_graph.to_dgl()
      dgl_graph.ndata["node_feat"] = node_feat
      dgl_graph.edata["edge_weight"] = new_graph.edge_features[
          "edge_weight"
      ]
      dgl_graph.graph_reg = new_graph.graph_attributes["graph_reg"]
      logits = self.gnn_clf(dgl_graph)
  
      return logits, dgl_graph
  
imports: import argparse
  import os
  import time
  import dgl
  import dgl.function
  import networkx
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.nn.functional
  from dgl import DGLGraph
  from dgl.data import load_data, register_data_args
  from scipy import sparse
  from ...modules.graph_construction import NodeEmbeddingBasedGraphConstruction, NodeEmbeddingBasedRefinedGraphConstruction
  from ...modules.utils.vocab_utils import VocabModel
  from ..utils import EarlyStopping
label_desc: Use image node_id 2 for calling the DynamicGNNClassifier obj's underlying member method code with example usage: obj.forward(graph) and returns: logits,  dgl_graph