code: def tf_keras_iris_explainer(models, iris_data, rf_classifier):
      # Define explainer constants
      LATENT_DIM = 2
      COEFF_SPARSITY = 0.0
      COEFF_CONSISTENCY = 0.0
      TRAIN_STEPS = 100
      BATCH_SIZE = 100
  
      # Define encoder. `tanh` is added to work with DDPG.
      encoder = keras.Sequential(
          [models[1], keras.layers.Activation(tf.math.tanh)]
      )
  
      # Define decoder. `atanh` is added to work with DDPG
      decoder = keras.Sequential(
          [keras.layers.Activation(tf.math.atan), models[0].layers[2]]
      )
  
      # need to define a wrapper for the decoder to return a list of tensors
      class DecoderList(tf.keras.Model):
          def __init__(self, decoder: tf.keras.Model, **kwargs):
              super().__init__(**kwargs)
              self.decoder = decoder
  
          def call(
              self, input: Union[tf.Tensor, List[tf.Tensor]], **kwargs
          ):
              return [self.decoder(input, **kwargs)]
  
      # Redefine the call method to return a list of tensors.
      decoder = DecoderList(decoder)
  
      # Define predictor.
      predictor = lambda x: rf_classifier[0].predict_proba(
          x
      )  # noqa: E731
  
      # Define explainer.
      explainer = CounterfactualRLTabular(
          encoder=encoder,
          decoder=decoder,
          latent_dim=LATENT_DIM,
          encoder_preprocessor=lambda x: x,
          decoder_inv_preprocessor=lambda x: x,
          predictor=predictor,
          coeff_sparsity=COEFF_SPARSITY,
          coeff_consistency=COEFF_CONSISTENCY,
          category_map=iris_data["metadata"].get(
              "category_map", dict()
          ),
          feature_names=iris_data["metadata"].get("feature_names"),
          ranges=dict(),
          immutable_features=[],
          conditional_func=lambda x: None,
          train_steps=TRAIN_STEPS,
          batch_size=BATCH_SIZE,
          backend="tensorflow",
      )
  
      return explainer
  
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 7 for calling a global function with example usage: tf_keras_iris_explainer(models, iris_data, rf_classifier) and returns: explainer, list