code: def encode_init_node_feature(self, data):
      # graph embedding initialization
      batch_gd = self.g2s.graph_initializer(data)
      return batch_gd
  
imports: import argparse
  import multiprocessing
  import os
  import platform
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn
  import torch.optim
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.squad import SQuADDataset
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.evaluation import BLEU, METEOR, ROUGE
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.config_utils import get_yaml_config, update_values
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, grid, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from .fused_embedding_construction import FusedEmbeddingConstruction
label_desc: Use image node_id 2 for calling the QGModel obj's underlying member method code with example usage: obj.encode_init_node_feature(data) and returns: batch_gd