code: def eval_log_regression_with_model(
      model,
      train_dataset_str="ImageNet:split=TRAIN",
      val_dataset_str="ImageNet:split=VAL",
      finetune_dataset_str=None,
      autocast_dtype=torch.float,
      finetune_on_val=False,
      metric_type=MetricType.MEAN_ACCURACY,
      train_dtype=torch.float64,
      train_features_device=_CPU_DEVICE,
      max_train_iters=DEFAULT_MAX_ITER,
  ):
      cudnn.benchmark = True
  
      transform = make_classification_eval_transform(resize_size=224)
      target_transform = None
  
      train_dataset = make_dataset(
          dataset_str=train_dataset_str,
          transform=transform,
          target_transform=target_transform,
      )
      val_dataset = make_dataset(
          dataset_str=val_dataset_str,
          transform=transform,
          target_transform=target_transform,
      )
      if finetune_dataset_str is not None:
          finetune_dataset = make_dataset(
              dataset_str=finetune_dataset_str,
              transform=transform,
              target_transform=target_transform,
          )
      else:
          finetune_dataset = None
  
      with torch.cuda.amp.autocast(dtype=autocast_dtype):
          results_dict_logreg = eval_log_regression(
              model=model,
              train_dataset=train_dataset,
              val_dataset=val_dataset,
              finetune_dataset=finetune_dataset,
              metric_type=metric_type,
              batch_size=256,
              num_workers=0,  # 5,
              finetune_on_val=finetune_on_val,
              train_dtype=train_dtype,
              train_features_device=train_features_device,
              max_train_iters=max_train_iters,
          )
  
      results_dict = {
          "top-1": results_dict_logreg["top-1"].cpu().numpy() * 100.0,
          "top-5": results_dict_logreg.get("top-5", torch.tensor(0.0))
          .cpu()
          .numpy()
          * 100.0,
          "best_C": results_dict_logreg["best_C"],
      }
      logger.info(
          "\n".join(
              [
                  "Training of the supervised logistic regression on frozen features completed.\n"
                  "Top-1 test accuracy: {acc:.1f}".format(
                      acc=results_dict["top-1"]
                  ),
                  "Top-5 test accuracy: {acc:.1f}".format(
                      acc=results_dict["top-5"]
                  ),
                  "obtained for C = {c:.6f}".format(
                      c=results_dict["best_C"]
                  ),
              ]
          )
      )
  
      torch.distributed.barrier()
      return results_dict
  
imports: import argparse
  import gc
  import logging
  import sys
  import time
  from typing import List, Optional
  from cuml.linear_model import LogisticRegression
  import torch
  import torch.backends.cudnn
  import torch.distributed
  from torch import nn
  from torch.utils.data import TensorDataset
  from torchmetrics import MetricTracker
  from dinov2.data import make_dataset
  from dinov2.data.transforms import make_classification_eval_transform
  from dinov2.distributed import get_global_rank, get_global_size
  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 evaluate, extract_features
  from dinov2.utils.dtype import as_torch_dtype
label_desc: Use image node_id 7 for calling a global function with example usage: eval_log_regression_with_model(model, train_dataset_str, val_dataset_str, finetune_dataset_str, autocast_dtype, finetune_on_val, metric_type, train_dtype, train_features_device, max_train_iters) and returns: results_dict