code: def test_single_dataset(iris_split_dataset_and_model_custom):
      (
          iris_train,
          iris_test,
          iris_model,
      ) = iris_split_dataset_and_model_custom
      suite = suites.full_suite()
      res_train = suite.run(
          iris_train,
          iris_test,
          iris_model,
          with_display=False,
          run_single_dataset="Train",
      )
      expected_train_headers = [
          "Train Test Performance",
          "Feature Label Correlation Change",
          "Feature Label Correlation - Train Dataset",
          "Feature-Feature Correlation - Train Dataset",
          "Weak Segments Performance - Train Dataset",
          "ROC Report - Train Dataset",
          "Prediction Drift",
          "Simple Model Comparison",
          "Unused Features - Train Dataset",
          "Model Inference Time - Train Dataset",
          "Datasets Size Comparison",
          "New Label Train Test",
          "New Category Train Test",
          "String Mismatch Comparison",
          "Train Test Samples Mix",
          "Feature Drift",
          "Label Drift",
          "Multivariate Drift",
          "Single Value in Column - Train Dataset",
          "Special Characters - Train Dataset",
          "Mixed Nulls - Train Dataset",
          "Mixed Data Types - Train Dataset",
          "String Mismatch - Train Dataset",
          "Data Duplicates - Train Dataset",
          "String Length Out Of Bounds - Train Dataset",
          "Conflicting Labels - Train Dataset",
          "Confusion Matrix Report - Train Dataset",
          "Calibration Metric - Train Dataset",
          "Outlier Sample Detection - Train Dataset",
          "Regression Error Distribution - Train Dataset",
          "Boosting Overfit",
          "Date Train Test Leakage Duplicates",
          "Date Train Test Leakage Overlap",
          "Index Train Test Leakage",
          "Identifier Label Correlation - Train Dataset",
      ]
  
      res_test = suite.run(
          iris_train,
          iris_test,
          iris_model,
          with_display=False,
          run_single_dataset="Test",
      )
      res_full = suite.run(
          iris_train, iris_test, iris_model, with_display=False
      )
      res_names = [x.get_header() for x in res_train.results]
      assert_that(res_names, contains_exactly(*expected_train_headers))
      assert_that(res_test.results, has_length(35))
      assert_that(res_full.results, has_length(54))
  
imports: import typing
  from datetime import datetime
  import pandas
  import pytest
  from catboost import CatBoostClassifier, CatBoostRegressor
  from hamcrest import assert_that, contains_exactly, has_length
  from lightgbm import LGBMClassifier, LGBMRegressor
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.model_selection import train_test_split
  from xgboost import XGBClassifier, XGBRegressor
  from deepchecks.tabular import Dataset, suites
  from tests.common import get_expected_results_length, validate_suite_result
label_desc: Use image node_id 18 for calling a global function with example usage: test_single_dataset(iris_split_dataset_and_model_custom) without return types