code: def get_quantiles(
      features: pd.DataFrame,
      sensitive_attribute: pd.Series,
      definition: int,
  ) -> tuple[dict, dict]:
      """
      Calculates the quantiles in the dataset for each feature and group.
  
      Parameters
      ----------
      features : pd.DataFrame
          The feature matrix.
      sensitive_attribute : pd.Series
          The sensitive attribute.
  
      Returns
      -------
      dict, dict
          The quantiles per group and the global quantiles.
      """
      quantiles = np.linspace(0, 1, definition)
      # Create a dictionary with quantile values per group.
      group_quantiles = {}
      # Also create a dictionary with the global quantiles.
      global_quantiles = {}
      for column in features.columns:
          global_quantiles[column] = (
              features[column].quantile(quantiles).values
          )
  
          # Get the quantiles for each group.
          group_quantiles[column] = _get_group_quantiles(
              features[column], sensitive_attribute, quantiles
          )
      return group_quantiles, global_quantiles
  
imports: import numpy
  import pandas
label_desc: Use image node_id 1 for calling a global function with example usage: get_quantiles(features, sensitive_attribute, definition) and returns: group_quantiles,  global_quantiles