code: def wrapper(tmp_path):
      """A simple wrapper around random-forest model"""
      data = create_dataset(grayscale=False)
      wrapper = SklearnImageClassificationWrapper(
          "random-forest", {}, tmp_path
      )
      wrapper._train(
          train_data=data,
          val_data=None,
          test_data=None,
          writer=SummaryWriter(write_to_disk=False),
          experiment=DummyExperiment(),
          dry_run=True,
      )
      return wrapper
  
file_path: traintool/tests/image_classification/test_sklearn_models.py
imports: import comet_ml
  import pytest
  import numpy
  from sklearn.ensemble import RandomForestClassifier
  from tensorboardX import SummaryWriter
  from conftest import create_dataset, create_image
  from traintool.image_classification import SklearnImageClassificationWrapper
  from traintool.utils import DummyExperiment
label_desc: Use image node_id 1 for calling a global function with example usage: wrapper(tmp_path) and returns: wrapper