class_docstr: Abstract class for Low dimensional embedding. The class defines
  uniform API, which is used by the NNContrastive explainer internally.
  NNContrastive explainer uses BaseEmbedding to obtain low dimensional
  vector embedding of the data, which is used for Neighborhood query.
class_docstr_tok: ["Abstract","class","for","Low","dimensional","embedding",".","The","class","defines","uniform","API",",","which","is","used","by","the","NNContrastive","explainer","internally",".","NNContrastive","explainer","uses","BaseEmbedding","to","obtain","low","dimensional","vector","embedding","of","the","data",",","which","is","used","for","Neighborhood","query","."]
code: def numerical_features(
      self, return_index: bool = False
  ) -> Union[List[str], List[int]]:
      return [
          i if return_index else feat
          for i, feat in enumerate(self._features)
          if feat not in self._categorical_features
      ]
  
imports: import numpy
  import pandas
  from abc import ABC, abstractmethod
  from typing import Union, List, Any
  from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, QuantileTransformer, StandardScaler, MinMaxScaler
label_desc: Use image node_id 5 for calling the BaseEmbedding obj's underlying member method code with example usage: obj.numerical_features(return_index) and returns: unknown