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 build(
      self,
      input_shape: int,
  ):
      try:
          import tensorflow as tf
      except ImportError:
          raise RuntimeError(
              f"Error: can not import tensorflow library!"
          )
  
      embedding_dim = self._config.get("embedding_dim")
  
      self._encoder = tf.keras.Sequential(name="encoder")
      for i, lyr in enumerate(self._config.get("layers_config")):
          self._encoder.add(
              tf.keras.layers.Dense(
                  int(lyr),
                  activation=self._config.get("encoder_activation"),
                  kernel_regularizer=self._config.get(
                      "encoder_kernel_regularizer"
                  ),
                  bias_initializer=self._config.get(
                      "encoder_bias_initializer"
                  ),
                  kernel_initializer=self._config.get(
                      "encoder_kernel_initializer"
                  ),
                  activity_regularizer=self._config.get(
                      "encoder_activity_regularizer"
                  ),
                  name=f"encoder_layer_{i+1}",
              )
          )
      self._encoder.add(
          tf.keras.layers.Dense(
              int(embedding_dim),
              activation=self._config.get("embedding_activation"),
              kernel_regularizer=self._config.get(
                  "encoder_kernel_regularizer"
              ),
              bias_initializer=self._config.get(
                  "encoder_bias_initializer"
              ),
              kernel_initializer=self._config.get(
                  "encoder_kernel_initializer"
              ),
              activity_regularizer=self._config.get(
                  "embedding_activity_regularizer"
              ),
              name=f"embedding_layer",
          )
      )
  
      self._decoder = tf.keras.Sequential(name="decoder")
      for i, lyr in enumerate(self._config.get("layers_config")[::-1]):
          self._decoder.add(
              tf.keras.layers.Dense(
                  int(lyr),
                  activation=self._config.get("decoder_activation"),
                  kernel_regularizer=self._config.get(
                      "decoder_kernel_regularizer"
                  ),
                  bias_initializer=self._config.get(
                      "decoder_bias_initializer"
                  ),
                  kernel_initializer=self._config.get(
                      "decoder_kernel_initializer"
                  ),
                  activity_regularizer=self._config.get(
                      "decoder_activity_regularizer"
                  ),
                  name=f"decoder_layer_{i+1}",
              )
          )
      self._decoder.add(
          tf.keras.layers.Dense(
              input_shape,
              activation=self._config.get(
                  "decoder_last_layer_activation"
              ),
              kernel_regularizer=self._config.get(
                  "decoder_kernel_regularizer"
              ),
              bias_initializer=self._config.get(
                  "decoder_bias_initializer"
              ),
              kernel_initializer=self._config.get(
                  "decoder_kernel_initializer"
              ),
              activity_regularizer=self._config.get(
                  "decoder_activity_regularizer"
              ),
              name=f"decode_layer",
          )
      )
  
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 3 for calling the AEEmbedding obj's underlying member method code with example usage: obj.build(input_shape) without return types