code: def similarity_explainer(ffn_classifier, iris_data):
      criterion = tf.keras.losses.SparseCategoricalCrossentropy(
          from_logits=True
      )
      explainer = GradientSimilarity(
          predictor=ffn_classifier,
          loss_fn=criterion,
          precompute_grads=True,
          backend="tensorflow",
          sim_fn="grad_cos",
      )
      explainer.fit(
          X_train=iris_data["X_train"], Y_train=iris_data["y_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 21 for calling a global function with example usage: similarity_explainer(ffn_classifier, iris_data) and returns: explainer