code: def print_config(config):
      print("**************** MODEL CONFIGURATION ****************")
      for key in sorted(config.keys()):
          val = config[key]
          keystr = "{}".format(key) + (" " * (24 - len(key)))
          print("{} -->   {}".format(keystr, val))
      print("**************** MODEL CONFIGURATION ****************")
  
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 4 for calling a global function with example usage: print_config(config) without return types