code: def importance(
      data,
      target: str,
      preprocess_func=None,
      estimator=None,
      return_values: bool = False,
      truncate: bool = True,
      top_features: Optional[int] = None,
      compute_backend: Optional[str] = None,
      viz_backend: Optional[str] = None,
      **kwargs,
  ):
      """Variable importance chart.
  
      This feature utilizes fits a simple model to the dataset to generate an estimate
      of feature importance (predictive power). Note that these results are dependent on
      the accuracy of the fitted model and should refined during modeling.
  
      Args:
          data: A Pandas data frame
          target: Name of the response column, as a string
          preprocess_func: A custom preprocessing function that takes a Pandas dataframe and the target/response column as a string. Returns X and y as tuple.
          estimator: A custom sklearn estimator. Default is Random Forest Classifier
          return_values: If True, only the importance values as a numpy array
          truncate: If True, negative importance values will be truncated (set to zero)
          top_features: Return the top N most important features. Default is None (all features)
          compute_backend: The compute backend
          viz_backend: The visualization backend
          **kwargs: Other arguments to be passed to the preprocess function
  
      Returns:
          Matplotlib figure
      """
      importance_values, idx, cols = _get_compute_backend(
          compute_backend, data
      ).compute_importance(
          data, target, preprocess_func, estimator, truncate, **kwargs
      )
  
      if return_values:
          return importance_values
      else:
          top_features = top_features or len(cols)
          return _get_viz_backend(viz_backend).viz_importance(
              importance_values, idx[:top_features], cols
          )
  
file_path: data-describe/data_describe/core/importance.py
imports: from typing import Optional
  import numpy
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.inspection import permutation_importance
  import seaborn
  import matplotlib.pyplot
  from data_describe.config._config import get_option
  from data_describe.misc.preprocessing import preprocess
  from data_describe.backends import _get_viz_backend, _get_compute_backend
label_desc: Use image node_id 1 for calling a global function with example usage: importance(data, target, preprocess_func, estimator, return_values, truncate, top_features, compute_backend, viz_backend) and returns: importance_values, _get_viz_backend