code: def run_eval_linear(
      model,
      output_dir,
      train_dataset_str,
      val_dataset_str,
      batch_size,
      epochs,
      epoch_length,
      num_workers,
      save_checkpoint_frequency,
      eval_period_iterations,
      learning_rates,
      autocast_dtype,
      test_dataset_strs=None,
      resume=True,
      classifier_fpath=None,
      val_class_mapping_fpath=None,
      test_class_mapping_fpaths=[None],
      val_metric_type=MetricType.MEAN_ACCURACY,
      test_metric_types=None,
  ):
      seed = 0
  
      if test_dataset_strs is None:
          test_dataset_strs = [val_dataset_str]
      if test_metric_types is None:
          test_metric_types = [val_metric_type] * len(test_dataset_strs)
      else:
          assert len(test_metric_types) == len(test_dataset_strs)
      assert len(test_dataset_strs) == len(test_class_mapping_fpaths)
  
      train_transform = make_classification_train_transform()
      train_dataset = make_dataset(
          dataset_str=train_dataset_str,
          transform=train_transform,
      )
      training_num_classes = len(
          torch.unique(
              torch.Tensor(train_dataset.get_targets().astype(int))
          )
      )
      sampler_type = SamplerType.SHARDED_INFINITE
      # sampler_type = SamplerType.INFINITE
  
      n_last_blocks_list = [1, 4]
      n_last_blocks = max(n_last_blocks_list)
      autocast_ctx = partial(
          torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype
      )
      feature_model = ModelWithIntermediateLayers(
          model, n_last_blocks, autocast_ctx
      )
      sample_output = feature_model(
          train_dataset[0][0].unsqueeze(0).cuda()
      )
  
      linear_classifiers, optim_param_groups = setup_linear_classifiers(
          sample_output,
          n_last_blocks_list,
          learning_rates,
          batch_size,
          training_num_classes,
      )
  
      optimizer = torch.optim.SGD(
          optim_param_groups, momentum=0.9, weight_decay=0
      )
      max_iter = epochs * epoch_length
      scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
          optimizer, max_iter, eta_min=0
      )
      checkpointer = Checkpointer(
          linear_classifiers,
          output_dir,
          optimizer=optimizer,
          scheduler=scheduler,
      )
      start_iter = (
          checkpointer.resume_or_load(
              classifier_fpath or "", resume=resume
          ).get("iteration", -1)
          + 1
      )
      train_data_loader = make_data_loader(
          dataset=train_dataset,
          batch_size=batch_size,
          num_workers=num_workers,
          shuffle=True,
          seed=seed,
          sampler_type=sampler_type,
          sampler_advance=start_iter,
          drop_last=True,
          persistent_workers=True,
      )
      val_data_loader = make_eval_data_loader(
          val_dataset_str, batch_size, num_workers, val_metric_type
      )
  
      checkpoint_period = save_checkpoint_frequency * epoch_length
  
      if val_class_mapping_fpath is not None:
          logger.info(
              f"Using class mapping from {val_class_mapping_fpath}"
          )
          val_class_mapping = np.load(val_class_mapping_fpath)
      else:
          val_class_mapping = None
  
      test_class_mappings = []
      for class_mapping_fpath in test_class_mapping_fpaths:
          if (
              class_mapping_fpath is not None
              and class_mapping_fpath != "None"
          ):
              logger.info(
                  f"Using class mapping from {class_mapping_fpath}"
              )
              class_mapping = np.load(class_mapping_fpath)
          else:
              class_mapping = None
          test_class_mappings.append(class_mapping)
  
      metrics_file_path = os.path.join(
          output_dir, "results_eval_linear.json"
      )
      (
          val_results_dict,
          feature_model,
          linear_classifiers,
          iteration,
      ) = eval_linear(
          feature_model=feature_model,
          linear_classifiers=linear_classifiers,
          train_data_loader=train_data_loader,
          val_data_loader=val_data_loader,
          metrics_file_path=metrics_file_path,
          optimizer=optimizer,
          scheduler=scheduler,
          output_dir=output_dir,
          max_iter=max_iter,
          checkpoint_period=checkpoint_period,
          running_checkpoint_period=epoch_length,
          eval_period=eval_period_iterations,
          metric_type=val_metric_type,
          training_num_classes=training_num_classes,
          resume=resume,
          val_class_mapping=val_class_mapping,
          classifier_fpath=classifier_fpath,
      )
      results_dict = {}
      if (
          len(test_dataset_strs) > 1
          or test_dataset_strs[0] != val_dataset_str
      ):
          results_dict = test_on_datasets(
              feature_model,
              linear_classifiers,
              test_dataset_strs,
              batch_size,
              0,  # num_workers,
              test_metric_types,
              metrics_file_path,
              training_num_classes,
              iteration,
              val_results_dict["best_classifier"]["name"],
              prefixstring="",
              test_class_mappings=test_class_mappings,
          )
      results_dict["best_classifier"] = val_results_dict[
          "best_classifier"
      ]["name"]
      results_dict[f"{val_dataset_str}_accuracy"] = (
          100.0 * val_results_dict["best_classifier"]["accuracy"]
      )
      logger.info("Test Results Dict " + str(results_dict))
  
      return results_dict
  
imports: import argparse
  from functools import partial
  import json
  import logging
  import os
  import sys
  from typing import List, Optional
  import numpy
  import torch
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
  from dinov2.data import SamplerType, make_data_loader, make_dataset
  from dinov2.data.transforms import make_classification_eval_transform, make_classification_train_transform
  import dinov2.distributed
  from dinov2.eval.metrics import MetricType, build_metric
  from dinov2.eval.setup import get_args_parser
  from dinov2.eval.setup import setup_and_build_model
  from dinov2.eval.utils import ModelWithIntermediateLayers, evaluate
  from dinov2.logging import MetricLogger
label_desc: Use image node_id 12 for calling a global function with example usage: run_eval_linear(model, output_dir, train_dataset_str, val_dataset_str, batch_size, epochs, epoch_length, num_workers, save_checkpoint_frequency, eval_period_iterations, learning_rates, autocast_dtype, test_dataset_strs, resume, classifier_fpath, val_class_mapping_fpath, test_class_mapping_fpaths, val_metric_type, test_metric_types) and returns: results_dict