code: def naive_encoder(
      numerical_features, cat_features
  ) -> TransformerMixin:
      """Create a naive encoder for categorical and numerical features.
  
      The encoder handles nans for all features and uses label encoder for categorical features. Then, all features are
      scaled using RobustScaler.
  
      Parameters
      ----------
      numerical_features
      cat_features
  
      Returns
      -------
      TransformerMixin
          A transformer object
      """
      return ColumnTransformer(
          transformers=[
              (
                  "num",
                  Pipeline(
                      [
                          ("nan_handling", SimpleImputer()),
                          ("norm", RobustScaler()),
                      ]
                  ),
                  np.array(numerical_features, dtype="object"),
              ),
              (
                  "cat",
                  Pipeline(
                      [
                          (
                              "nan_handling",
                              SimpleImputer(strategy="most_frequent"),
                          ),
                          (
                              "encode",
                              run_available_kwargs(
                                  OrdinalEncoder,
                                  handle_unknown="use_encoded_value",
                                  unknown_value=-1,
                              ),
                          ),
                          ("norm", RobustScaler()),
                      ]
                  ),
                  np.array(cat_features, dtype="object"),
              ),
          ]
      )
  
file_path: deepchecks/deepchecks/tabular/checks/model_evaluation/unused_features.py
imports: import numpy
  import pandas
  import plotly.graph_objects
  from sklearn.base import TransformerMixin
  from sklearn.compose import ColumnTransformer
  from sklearn.decomposition import PCA
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import OrdinalEncoder, RobustScaler
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.utils.function import run_available_kwargs
label_desc: Use image node_id 1 for calling a global function with example usage: naive_encoder(numerical_features, cat_features) and returns: ColumnTransformer