code: def cfrl_explainer(rf_classifier, iris_ae, iris_data):
      # define explainer constants
      LATENT_DIM = 2
      COEFF_SPARSITY = 0.1
      COEFF_CONSISTENCY = 0.0
      TRAIN_STEPS = 100
      BATCH_SIZE = 100
  
      # 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.
      iris_ae.decoder = DecoderList(iris_ae.decoder)
  
      # define predictor.
      predictor = lambda x: rf_classifier[0].predict_proba(
          x
      )  # noqa: E731
  
      # define explainer.
      explainer = CounterfactualRLTabular(
          encoder=iris_ae.encoder,
          decoder=iris_ae.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=[],
          train_steps=TRAIN_STEPS,
          batch_size=BATCH_SIZE,
          backend="tensorflow",
      )
  
      # fit the explainer
      explainer.fit(X=iris_data["X_train"])
      return explainer
  
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 20 for calling a global function with example usage: cfrl_explainer(rf_classifier, iris_ae, iris_data) and returns: explainer, list