code: def iris_ae(iris_data):
      from alibi.models.tensorflow.autoencoder import AE
  
      # define encoder
      encoder = tf.keras.Sequential(
          [tf.keras.layers.Dense(2), tf.keras.layers.Activation("tanh")]
      )
  
      # define decoder
      decoder = tf.keras.Sequential([tf.keras.layers.Dense(4)])
  
      # define autoencoder, compile and fit
      ae = AE(encoder=encoder, decoder=decoder)
      ae.compile(
          optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
          loss=tf.keras.losses.MeanSquaredError(),
      )
      ae.fit(iris_data["X_train"], iris_data["X_train"], epochs=1)
      return ae
  
imports: import numbers
  import sys
  import numpy
  from numpy.testing import assert_allclose
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from sklearn.linear_model import LogisticRegression
  from sklearn.ensemble import RandomForestClassifier
  import tempfile
  import tensorflow
  from typing import List, Union
  from alibi.explainers import ALE, AnchorImage, AnchorTabular, AnchorText, IntegratedGradients, KernelShap, TreeShap, CounterfactualRLTabular, GradientSimilarity
  from alibi.explainers.integrated_gradients import LayerState
  from alibi.saving import load_explainer
  from alibi_testing.data import get_adult_data, get_iris_data, get_movie_sentiment_data
  import alibi_testing
  from alibi.utils.download import spacy_model
  from alibi.explainers.tests.utils import predict_fcn
label_desc: Use image node_id 10 for calling a global function with example usage: iris_ae(iris_data) and returns: ae