code: def evaluate_model(
      *, logreg_model, logreg_metric, test_data_loader, device
  ):
      postprocessors = {"metrics": logreg_model}
      metrics = {"metrics": logreg_metric}
      return evaluate(
          nn.Identity(),
          test_data_loader,
          postprocessors,
          metrics,
          device,
      )
  
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 2 for calling a global function with example usage: evaluate_model() and returns: evaluate