code: def _get_baseline_model_interpretation_string(
      count_better: int,
      count_total: int,
      best_baseline_model: str,
      best_baseline_performance: float,
  ) -> str:
      percentage_better = count_better / count_total
      best_baseline_model = best_baseline_model.replace("()", "")
  
      if percentage_better == 0:
          return (
              "The mechanism is better or equally good than all %s baseline mechanisms."
              % str(count_total)
          )
      elif percentage_better < 0.2:
          return (
              str(count_better)
              + " of "
              + str(count_total)
              + " baseline mechanisms performed significantly better. The best baseline mechanism is: "
              + best_baseline_model
              + " with a CRPS of "
              + str(best_baseline_performance)
              + "."
          )
      elif 0.2 < percentage_better < 0.4:
          return (
              str(count_better)
              + " of "
              + str(count_total)
              + " baseline mechanisms performed significantly better. The best baseline "
              "mechanism is: "
              + best_baseline_model
              + " with a CRPS of "
              + str(best_baseline_performance)
              + ". Accordingly, the current mechanism could be improved using a "
              + best_baseline_model
              + " instead. If you are using an auto assigment, consider a "
              "better assignment quality level such as AssignmentQuality.BETTER."
          )
      else:
          return (
              str(count_better)
              + " of "
              + str(count_total)
              + " baseline mechanisms performed significantly better. The best baseline "
              "mechanism is: "
              + best_baseline_model
              + " with a CRPS of "
              + str(best_baseline_performance)
              + ". Seeing this, consider changing the mechanism to a "
              + best_baseline_model
              + " or, if you are using an auto assigment, consider a better assignment quality "
              "level such as AssignmentQuality.BETTER."
          )
  
imports: from dataclasses import dataclass
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import networkx
  import numpy
  import pandas
  from joblib import Parallel, delayed
  from numpy.matlib import repmat
  from scipy.stats import mode
  from sklearn.metrics import f1_score, mean_squared_error, r2_score
  from sklearn.model_selection import KFold
  from statsmodels.stats.multitest import multipletests
  from tqdm import tqdm
  from dowhy.gcm import ClassifierFCM, PostNonlinearModel, PredictionModel, ProbabilisticCausalModel, config, draw_samples, kernel_based
  from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ConditionalStochasticModel, InvertibleFunctionalCausalModel
  from dowhy.gcm.divergence import auto_estimate_kl_divergence
  from dowhy.gcm.falsify import EvaluationResult, falsify_graph, plot_evaluation_results
  from dowhy.gcm.ml import SklearnRegressionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_classifier, create_random_forest_regressor, create_ridge_regressor
  from dowhy.gcm.ml.classification import create_ada_boost_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier
  from dowhy.gcm.ml.regression import create_ada_boost_regressor, create_extra_trees_regressor, create_polynom_regressor
  from dowhy.gcm.stats import quantile_based_fwer
  from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d
  from dowhy.graph import get_ordered_predecessors, is_root_node
label_desc: Use image node_id 9 for calling a global function with example usage: _get_baseline_model_interpretation_string(count_better, count_total, best_baseline_model, best_baseline_performance) and returns: unknown, unknown, unknown, unknown