code: def main():
      parser = HfArgumentParser(
          (
              DataTrainingArguments,
              TeacherModelArguments,
              StudentModelArguments,
              DistillTrainingArguments,
          ),
          description=DESCRIPTION,
      )
  
      if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          (
              data_args,
              teacher_args,
              student_args,
              training_args,
          ) = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1])
          )
      else:
          (
              data_args,
              teacher_args,
              student_args,
              training_args,
          ) = parser.parse_args_into_dataclasses()
  
      # Detecting last checkpoint.
      last_checkpoint = None
      if (
          os.path.isdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          last_checkpoint = get_last_checkpoint(
              training_args.output_dir
          )
          if (
              last_checkpoint is None
              and len(os.listdir(training_args.output_dir)) > 0
          ):
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                  "Use --overwrite_output_dir to overcome."
              )
          elif last_checkpoint is not None:
              logger.info(
                  f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                  "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
              )
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          handlers=[logging.StreamHandler(sys.stdout)],
      )
      logger.setLevel(
          logging.INFO
          if is_main_process(training_args.local_rank)
          else logging.WARN
      )
  
      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
      )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank):
          utils.logging.set_verbosity_info()
          utils.logging.enable_default_handler()
          utils.logging.enable_explicit_format()
  
      if training_args.local_rank != -1:
          raise ValueError(
              "Distributed training is not currently supported."
          )
      if training_args.tpu_num_cores is not None:
          raise ValueError(
              "TPU acceleration is not currently supported."
          )
  
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # Set seed before initializing model.
      set_seed(training_args.seed)
  
      # 1. read in data
      examples = read_lines(data_args.data_file)
      class_names = read_lines(data_args.class_names_file)
  
      # 2. get teacher predictions and load into dataset
      logger.info("Generating predictions from zero-shot teacher model")
      teacher_soft_preds = get_teacher_predictions(
          teacher_args.teacher_name_or_path,
          examples,
          class_names,
          teacher_args.hypothesis_template,
          teacher_args.teacher_batch_size,
          teacher_args.temperature,
          teacher_args.multi_label,
          data_args.use_fast_tokenizer,
          training_args.no_cuda,
          training_args.fp16,
      )
      dataset = Dataset.from_dict(
          {
              "text": examples,
              "labels": teacher_soft_preds,
          }
      )
  
      # 3. create student
      logger.info("Initializing student model")
      model = AutoModelForSequenceClassification.from_pretrained(
          student_args.student_name_or_path, num_labels=len(class_names)
      )
      tokenizer = AutoTokenizer.from_pretrained(
          student_args.student_name_or_path,
          use_fast=data_args.use_fast_tokenizer,
      )
      model.config.id2label = {
          i: label for i, label in enumerate(class_names)
      }
      model.config.label2id = {
          label: i for i, label in enumerate(class_names)
      }
  
      # 4. train student on teacher predictions
      dataset = dataset.map(tokenizer, input_columns="text")
      dataset.set_format("torch")
  
      def compute_metrics(p, return_outputs=False):
          preds = p.predictions.argmax(-1)
          proxy_labels = p.label_ids.argmax(
              -1
          )  # "label_ids" are actually distributions
          return {"agreement": (preds == proxy_labels).mean().item()}
  
      trainer = DistillationTrainer(
          model=model,
          tokenizer=tokenizer,
          args=training_args,
          train_dataset=dataset,
          compute_metrics=compute_metrics,
      )
  
      if training_args.do_train:
          logger.info("Training student model on teacher predictions")
          trainer.train()
  
      if training_args.do_eval:
          agreement = trainer.evaluate(eval_dataset=dataset)[
              "eval_agreement"
          ]
          logger.info(
              f"Agreement of student and teacher predictions: {agreement * 100:0.2f}%"
          )
  
      trainer.save_model()
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import List, Optional
  import torch
  from datasets import Dataset
  from torch import nn
  from tqdm.auto import tqdm
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, set_seed, utils
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
label_desc: Use image node_id 5 for calling a global function with example usage: main() and returns: dict