code: def assert_classification_result(result, dataset: Dataset):
      for dataset_name in ["Test", "Train"]:
          dataset_df = result.loc[result["Dataset"] == dataset_name]
          for class_name in dataset.classes_in_label_col:
              class_df = dataset_df.loc[
                  dataset_df["Class"] == class_name
              ]
              for metric in MULTICLASS_SCORERS_NON_AVERAGE.keys():
                  metric_row = class_df.loc[
                      class_df["Metric"] == metric
                  ]
                  assert_that(
                      metric_row["Value"].iloc[0], close_to(1, 0.3)
                  )
  
imports: import re
  from typing import List
  import numpy
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_items, has_length, instance_of, raises
  from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
  from sklearn.metrics import jaccard_score, make_scorer
  from sklearn.model_selection import train_test_split
  from deepchecks.core import ConditionResult
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError, ModelValidationError
  from deepchecks.tabular.checks.model_evaluation import TrainTestPerformance
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.tabular.metric_utils.scorers import DEFAULT_REGRESSION_SCORERS, MULTICLASS_SCORERS_NON_AVERAGE
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 6 for calling a global function with example usage: assert_classification_result(result, dataset) without return types