code: def create_error_regression_model(
      numeric_features, cat_features, random_state=42
  ) -> Tuple[Pipeline, List[Hashable]]:
      """Create regression model to calculate error."""
      numeric_transformer = SimpleImputer()
      categorical_transformer = Pipeline(
          steps=[
              ("imputer", SimpleImputer(strategy="most_frequent")),
              ("encoder", TargetEncoder()),
          ]
      )
  
      preprocessor = ColumnTransformer(
          transformers=[
              ("num", numeric_transformer, numeric_features),
              ("cat", categorical_transformer, cat_features),
          ]
      )
  
      return (
          Pipeline(
              steps=[
                  ("preprocessing", preprocessor),
                  (
                      "model",
                      RandomForestRegressor(
                          max_depth=4,
                          n_jobs=-1,
                          random_state=random_state,
                      ),
                  ),
              ]
          ),
          numeric_features + cat_features,
      )
  
imports: from typing import Any, Callable, Dict, Hashable, List, Optional, Tuple
  import numpy
  import pandas
  import plotly.express
  from category_encoders import TargetEncoder
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.impute import SimpleImputer
  from sklearn.metrics import r2_score
  from sklearn.pipeline import Pipeline
  from sklearn.tree import DecisionTreeRegressor
  from deepchecks import tabular
  from deepchecks.core.errors import DeepchecksProcessError
  from deepchecks.tabular import Dataset
  from deepchecks.tabular.utils.feature_importance import _calculate_feature_importance
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.plot import colors
  from deepchecks.utils.strings import format_number, format_percent
label_desc: Use image node_id 2 for calling a global function with example usage: create_error_regression_model(numeric_features, cat_features, random_state) and returns: Pipeline,  unknown