code: def evaluate_causal_model(
      causal_model: ProbabilisticCausalModel,
      data: pd.DataFrame,
      max_num_samples: int = -1,
      evaluate_causal_mechanisms: bool = True,
      compare_mechanism_baselines: bool = False,
      evaluate_invertibility_assumptions: bool = True,
      evaluate_overall_kl_divergence: bool = True,
      evaluate_causal_structure: bool = True,
      config: Optional[EvaluateCausalModelConfig] = None,
  ) -> CausalModelEvaluationResult:
      """Evaluates the given causal model by running different evaluations.
  
      Evaluation of Causal Mechanisms:
      The quality of the causal mechanisms is assessed using k-fold cross validation. This means that the models are trained
      from scratch multiple times, which might take a significant amount of time for larger models. Within each fold, the models
      are assessed by different metrics. For all models, the continuous ranked probability score (CRPS) normalized by the
      standard deviation is estimated, an important metric that provides insights to the model performance as well as its
      calibration. Further, if the node is numerical, the mean squared error (MSE), the normalized MSE (normalized by
      the variance) and the R2 coefficient is computed. In case of categorical nodes, the F1 score is computed instead.
      Optionally, the mechanisms' CRPS are compared with baseline models to see if there are baseline models performing significantly better.
  
      Evaluation of Invertible Functional Causal Model Assumption:
      Invertible causal mechanisms rely on the assumption that the inputs are independent of the reconstructed noise.
      This is, assuming there are no hidden confounders, the noise should be independent of parents of a node. This
      can be evaluated by testing statistical independence between the reconstructed noise and the used input samples.
  
      Evaluation of Generated Distribution:
      The distribution generated by the causal model is compared with the observed data using KL divergence. To avoid
      estimating the KL divergence of high-dimensional data, we approximate it by calculating the mean KL divergence
      across the individual marginal KL divergences for each node.
  
      Evaluation of the Causal Graph Structure:
      The causal graph structure is evaluated by running a method to falsify the graph. The method involves conducting
      independence tests and may consume a significant amount of time for more extensive graphs. The results provide
      an indication of whether the graph is rejected or not. It's important to note that a non-rejected graph does not
      guarantee its correctness. It simply means that the evaluation did not find substantial evidence to refute the causal
      graph based on the provided data. However, a rejected graph might indicate potential issues with its structure.
  
      The outcomes of these evaluation methods should be interpreted with caution, and bad fits should not be
      over-interpreted. Nonetheless, the results can offer insights into the performance of the causal model and potential
      areas for improvement.
  
      :param causal_model: The causal model to evaluate.
      :param data: The data used for the evaluation.
      :param max_num_samples: The maximum number of samples used for the evaluation. If the runtime is too slow, consider
                              setting this to a smaller value. The default -1 indicates that all samples are used.
      :param evaluate_causal_mechanisms: If True, the causal mechanisms are evaluated.
      :param compare_mechanism_baselines: If True, the causal mechanisms are compared with baseline models to see
                                          if there are model choices that perform significantly better. If False, this
                                          comparison is skipped. This is ignored if evaluate_causal_mechanisms is False.
      :param evaluate_invertibility_assumptions: If True, the model assumption represented by invertible causal mechanisms
                                                 is tested.
      :param evaluate_overall_kl_divergence: If True, the KL divergence between the generated and the observed data is
                                             estimated.
      :param evaluate_causal_structure: If True, the causal graph structure is evaluated.
      :return: A summary of the evaluation.
      """
      if config is None:
          config = EvaluateCausalModelConfig()
  
      evaluation_result = CausalModelEvaluationResult()
  
      if max_num_samples >= 0 and max_num_samples < data.shape[0]:
          data = data[
              np.random.choice(
                  data.shape[0], data.shape[0], replace=False
              )
          ]
  
      if evaluate_causal_mechanisms:
          evaluation_result.mechanism_performances = (
              _evaluate_model_performances(
                  causal_model,
                  data,
                  compare_mechanism_baselines,
                  config.baseline_models_regression,
                  config.baseline_models_classification,
                  config.mechanism_evaluation_kfolds,
                  config.n_jobs,
              )
          )
  
      if evaluate_invertibility_assumptions:
          evaluation_result.pnl_assumptions = (
              _evaluate_invertibility_assumptions(
                  causal_model,
                  data,
                  config.independence_test_invertible,
                  config.significance_level_invertible,
                  config.fdr_control_method_invertible,
                  config.bootstrap_runs_invertible,
              )
          )
  
      if evaluate_overall_kl_divergence:
          # Normally, we need to estimate the KL divergence jointly. However, to avoid issues with high dimensional data,
          # we approximate it by taking the average over the marginal KL divergences.
          drawn_samples = draw_samples(causal_model, data.shape[0])
  
          evaluation_result.overall_kl_divergence = np.mean(
              [
                  auto_estimate_kl_divergence(
                      drawn_samples[node].to_numpy(),
                      data[node].to_numpy(),
                  )
                  for node in causal_model.graph.nodes
              ]
          )
  
      if evaluate_causal_structure:
          evaluation_result.graph_falsification = falsify_graph(
              causal_model.graph,
              data,
              n_permutations=config.max_num_permutations_falsify,
              independence_test=config.independence_test_falsify,
              conditional_independence_test=config.conditional_independence_test_falsify,
              significance_level=config.falsify_graph_significance_level,
              n_jobs=config.n_jobs,
              allow_data_subset=False,
          )
  
      return evaluation_result
  
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 1 for calling a global function with example usage: evaluate_causal_model(causal_model, data, max_num_samples, evaluate_causal_mechanisms, compare_mechanism_baselines, evaluate_invertibility_assumptions, evaluate_overall_kl_divergence, evaluate_causal_structure, config) and returns: evaluation_result