code: def test_explainer(tf_keras_iris_explainer, iris_data):
      explainer = tf_keras_iris_explainer
  
      # Check that the encoding representation is between [-1, 1]
      encoder_preprocessor = explainer.params["encoder_preprocessor"]
      encoder = explainer.params["encoder"]
      decoder = explainer.params["decoder"]
  
      Z = encoder(encoder_preprocessor(iris_data["X_train"]))
      assert tf.math.reduce_min(Z) >= -1 and tf.math.reduce_max(Z) <= 1
  
      X_hat = decoder(Z)
      assert isinstance(X_hat, list)
  
      # Fit the explainer
      explainer.fit(X=iris_data["X_train"])
  
      # Construct explanation object.
      explainer.explain(
          X=iris_data["X_test"], Y_t=np.array([2]), C=None
      )
  
imports: from typing import Union, List
  import pytest
  from pytest_lazyfixture import lazy_fixture
  import numpy
  from numpy.testing import assert_allclose
  import tensorflow
  import tensorflow.keras
  from alibi.explainers import CounterfactualRLTabular
  from alibi.explainers.backends.cfrl_base import get_hard_distribution
  from alibi.explainers.backends.cfrl_tabular import get_he_preprocessor, split_ohe, get_numerical_conditional_vector, get_categorical_conditional_vector, get_statistics, get_conditional_vector, sample
label_desc: Use image node_id 8 for calling a global function with example usage: test_explainer(tf_keras_iris_explainer, iris_data) without return types