code: def ffn_classifier(request):
      data = request.param
      inputs = tf.keras.Input(shape=data["X_train"].shape[1:])
      x = tf.keras.layers.Dense(20, activation="relu")(inputs)
      x = tf.keras.layers.Dense(20, activation="relu")(x)
      outputs = tf.keras.layers.Dense(
          config["output_dim"], activation=config["activation"]
      )(x)
      model = tf.keras.Model(inputs=inputs, outputs=outputs)
      model.compile(loss=config["loss"], optimizer="adam")
      model.fit(
          data["X_train"],
          tf.keras.utils.to_categorical(data["y_train"]),
          epochs=1,
      )
      return model
  
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 8 for calling a global function with example usage: ffn_classifier(request) and returns: model