code: def main(
      det_callback, tb_callback, model_args, data_args, training_args
  ):
      # 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)],
      )
  
      log_level = training_args.get_process_log_level()
      logger.setLevel(log_level)
      transformers.utils.logging.set_verbosity(log_level)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
  
      # Log on each process the small summary:
      logger.info(
          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}"
      )
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # 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, "
                  "use `--overwrite_output_dir` to train from scratch."
              )
  
      # Initialize our dataset and prepare it for the 'image-classification' task.
      if data_args.dataset_name is not None:
          dataset = load_dataset(
              data_args.dataset_name,
              data_args.dataset_config_name,
              cache_dir=model_args.cache_dir,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
      else:
          data_files = {}
          if data_args.train_dir is not None:
              data_files["train"] = os.path.join(
                  data_args.train_dir, "**"
              )
          if data_args.validation_dir is not None:
              data_files["validation"] = os.path.join(
                  data_args.validation_dir, "**"
              )
          dataset = load_dataset(
              "imagefolder",
              data_files=data_files,
              cache_dir=model_args.cache_dir,
              task="image-classification",
          )
  
      # If we don't have a validation split, split off a percentage of train as validation.
      data_args.train_val_split = (
          None
          if "validation" in dataset.keys()
          else data_args.train_val_split
      )
      if (
          isinstance(data_args.train_val_split, float)
          and data_args.train_val_split > 0.0
      ):
          split = dataset["train"].train_test_split(
              data_args.train_val_split
          )
          dataset["train"] = split["train"]
          dataset["validation"] = split["test"]
  
      # Prepare label mappings.
      # We'll include these in the model's config to get human readable labels in the Inference API.
      labels = dataset["train"].features["labels"].names
      label2id, id2label = dict(), dict()
      for i, label in enumerate(labels):
          label2id[label] = str(i)
          id2label[str(i)] = label
  
      # Load the accuracy metric from the datasets package
      metric = datasets.load_metric("accuracy")
  
      # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
      # predictions and label_ids field) and has to return a dictionary string to float.
      def compute_metrics(p):
          """Computes accuracy on a batch of predictions"""
          return metric.compute(
              predictions=np.argmax(p.predictions, axis=1),
              references=p.label_ids,
          )
  
      config = AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path,
          num_labels=len(labels),
          label2id=label2id,
          id2label=id2label,
          finetuning_task="image-classification",
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
          trust_remote_code=model_args.trust_remote_code,
      )
      model = AutoModelForImageClassification.from_pretrained(
          model_args.model_name_or_path,
          from_tf=bool(".ckpt" in model_args.model_name_or_path),
          config=config,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
          ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
      )
      feature_extractor = AutoFeatureExtractor.from_pretrained(
          model_args.feature_extractor_name
          or model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  
      # Define torchvision transforms to be applied to each image.
      if "shortest_edge" in feature_extractor.size:
          size = feature_extractor.size["shortest_edge"]
      else:
          size = (
              feature_extractor.size["height"],
              feature_extractor.size["width"],
          )
      normalize = Normalize(
          mean=feature_extractor.image_mean,
          std=feature_extractor.image_std,
      )
      _train_transforms = Compose(
          [
              RandomResizedCrop(size),
              RandomHorizontalFlip(),
              ToTensor(),
              normalize,
          ]
      )
      _val_transforms = Compose(
          [
              Resize(size),
              CenterCrop(size),
              ToTensor(),
              normalize,
          ]
      )
  
      def train_transforms(example_batch):
          """Apply _train_transforms across a batch."""
          example_batch["pixel_values"] = [
              _train_transforms(pil_img.convert("RGB"))
              for pil_img in example_batch["image"]
          ]
          return example_batch
  
      def val_transforms(example_batch):
          """Apply _val_transforms across a batch."""
          example_batch["pixel_values"] = [
              _val_transforms(pil_img.convert("RGB"))
              for pil_img in example_batch["image"]
          ]
          return example_batch
  
      if training_args.do_train:
          if "train" not in dataset:
              raise ValueError("--do_train requires a train dataset")
          if data_args.max_train_samples is not None:
              dataset["train"] = (
                  dataset["train"]
                  .shuffle(seed=training_args.seed)
                  .select(range(data_args.max_train_samples))
              )
          # Set the training transforms
          dataset["train"].set_transform(train_transforms)
  
      if training_args.do_eval:
          if "validation" not in dataset:
              raise ValueError(
                  "--do_eval requires a validation dataset"
              )
          if data_args.max_eval_samples is not None:
              dataset["validation"] = (
                  dataset["validation"]
                  .shuffle(seed=training_args.seed)
                  .select(range(data_args.max_eval_samples))
              )
          # Set the validation transforms
          dataset["validation"].set_transform(val_transforms)
  
      # Initalize our trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=dataset["train"]
          if training_args.do_train
          else None,
          eval_dataset=dataset["validation"]
          if training_args.do_eval
          else None,
          compute_metrics=compute_metrics,
          tokenizer=feature_extractor,
          data_collator=collate_fn,
      )
  
      trainer.add_callback(det_callback)
      trainer.add_callback(tb_callback)
  
      # Training
      if training_args.do_train:
          checkpoint = None
          if training_args.resume_from_checkpoint is not None:
              checkpoint = training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
              checkpoint = last_checkpoint
  
          with dsat.dsat_reporting_context(
              core_context, op=det_callback.current_op
          ):
              train_result = trainer.train(
                  resume_from_checkpoint=checkpoint
              )
          trainer.save_model()
          trainer.log_metrics("train", train_result.metrics)
          trainer.save_metrics("train", train_result.metrics)
          trainer.save_state()
  
          print("Done with training. Starting eval.")
      # Evaluation
      if training_args.do_eval:
          metrics = trainer.evaluate()
          trainer.log_metrics("eval", metrics)
          trainer.save_metrics("eval", metrics)
  
      # Write model card and (optionally) push to hub
      kwargs = {
          "finetuned_from": model_args.model_name_or_path,
          "tasks": "image-classification",
          "dataset": data_args.dataset_name,
          "tags": ["image-classification", "vision"],
      }
      if training_args.push_to_hub:
          trainer.push_to_hub(**kwargs)
      else:
          trainer.create_model_card(**kwargs)
  
imports: import json
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from pathlib import Path
  from typing import Any, Dict, Optional, Tuple
  import datasets
  import numpy
  import torch
  import transformers
  from datasets import load_dataset
  from PIL import Image
  from torch.utils.tensorboard import SummaryWriter
  from torchvision.transforms import CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor
  from transformers import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoFeatureExtractor, AutoModelForImageClassification, HfArgumentParser, Trainer, TrainingArguments
  from transformers.integrations import TensorBoardCallback
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils.versions import require_version
  import determined
  from determined.pytorch import dsat
  from determined.transformers import DetCallback
label_desc: Use image node_id 5 for calling a global function with example usage: main(det_callback, tb_callback, model_args, data_args, training_args) and returns: metric, example_batch, example_batch