class_docstr: JoinAEEmbedding is a supervised auto-encoder architecture. It expects
  a class label with each input data. It jointly trains an auto-encoder and
  a classifier model. The classifier model classifies the obtained embedding
  by the provided class label.
class_docstr_tok: ["JoinAEEmbedding","is","a","supervised","auto-encoder","architecture",".","It","expects","a","class","label","with","each","input","data",".","It","jointly","trains","an","auto-encoder","and","a","classifier","model",".","The","classifier","model","classifies","the","obtained","embedding","by","the","provided","class","label","."]
code: def build(
      self,
      input_shape: int,
      n_classes: int,
  ):
      try:
          import tensorflow as tf
      except ImportError:
          raise RuntimeError(
              f"Error: can not import tensorflow library!"
          )
  
      super(JointAEEmbedding, self).build(input_shape=input_shape)
      self._classifier = tf.keras.Sequential(name="classifier")
      for i, lyr in enumerate(self._config.get("classifier_layers")):
          self._classifier.add(
              tf.keras.layers.Dense(
                  int(lyr),
                  activation=self._config.get("classifier_activation"),
                  kernel_regularizer=self._config.get(
                      "classifier_kernel_regularizer"
                  ),
                  kernel_initializer=self._config.get(
                      "classifier_kernel_initializer"
                  ),
                  bias_initializer=self._config.get(
                      "classifier_bias_initializer"
                  ),
                  name=f"classifier_layer_{i+1}",
              )
          )
      self._classifier.add(
          tf.keras.layers.Dense(
              int(n_classes),
              activation="softmax",
              name="class_activation",
          )
      )
  
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 2 for calling the JointAEEmbedding obj's underlying member method code with example usage: obj.build(input_shape, n_classes) without return types