code: def set_seed(args):
      random.seed(args.seed)
      np.random.seed(args.seed)
      torch.manual_seed(args.seed)
      if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed)
  
imports: import argparse
  import glob
  import json
  import logging
  import os
  import random
  import numpy
  import torch
  from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
  from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
  from torch import nn
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm, trange
  import transformers
  from transformers import WEIGHTS_NAME, AdamW, AlbertConfig, AlbertTokenizer, BertConfig, BertTokenizer, get_linear_schedule_with_warmup
  from transformers import glue_compute_metrics
  from transformers import glue_convert_examples_to_features
  from transformers import glue_output_modes
  from transformers import glue_processors
  from transformers.trainer_utils import is_main_process
label_desc: Use image node_id 1 for calling a global function with example usage: set_seed(args) without return types