class_docstr: AEEmbedding is an implementation of BaseEmbedding. AEEmbedding implements
  encoder-decoder architecture. This implementation is used by the NNContrastive
  explainer, to derive low-dimensional vector embedding in an unsupervised fashion.
class_docstr_tok: ["AEEmbedding","is","an","implementation","of","BaseEmbedding",".","AEEmbedding","implements","encoder-decoder","architecture",".","This","implementation","is","used","by","the","NNContrastive","explainer",",","to","derive","low-dimensional","vector","embedding","in","an","unsupervised","fashion","."]
code: def ae_loss(
      self,
      x: Union[np.ndarray, pd.DataFrame],
  ):
      if not self.is_fitted:
          raise RuntimeError(
              f"Error: aeloss can not be called prior to model fitting!"
          )
      x = self.preprocess(x, fit=False)
      xr = self._decoder(self._encoder(x)).numpy()
      return np.sqrt(np.mean(np.square(x - xr), axis=1))
  
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 6 for calling the AEEmbedding obj's underlying member method code with example usage: obj.ae_loss(x) and returns: np