code: def forward(self, graph_list, tgt=None, require_loss=True):
      # graph embedding initialization
      batch_gd = self.graph_initializer(graph_list)
  
      # run dynamic graph construction if turned on
      if hasattr(self, "graph_topology") and hasattr(
          self.graph_topology, "dynamic_topology"
      ):
          batch_gd = self.graph_topology.dynamic_topology(batch_gd)
  
      # run GNN
      self.gnn(batch_gd)
  
      # run graph classifier
      self.clf(batch_gd)
      logits = batch_gd.graph_attributes["logits"]
  
      if require_loss:
          loss = self.loss(logits, tgt)
          return logits, loss
      else:
          return logits
  
imports: import argparse
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import yaml
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.trec import TrecDataset
  from graph4nlp.pytorch.modules.evaluation.accuracy import Accuracy
  from graph4nlp.pytorch.modules.graph_construction import NodeEmbeddingBasedGraphConstruction, NodeEmbeddingBasedRefinedGraphConstruction
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.graph_embedding_initialization.graph_embedding_initialization import GraphEmbeddingInitialization
  from graph4nlp.pytorch.modules.graph_embedding_learning import GAT, GGNN, GraphSAGE
  from graph4nlp.pytorch.modules.loss.general_loss import GeneralLoss
  from graph4nlp.pytorch.modules.prediction.classification.graph_classification import FeedForwardNN
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, grid, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
label_desc: Use image node_id 2 for calling the TextClassifier obj's underlying member method code with example usage: obj.forward(graph_list, tgt, require_loss) and returns: logits,  loss, logits