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 save_model(self, filename: str):
      pass
  
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 8 for calling the AEEmbedding obj's underlying member method code with example usage: obj.save_model(filename) without return types