code: def ffn_model_multi_inputs(request):
      """
      Simple multi-inputs feed-forward model with configurable data, loss function, output activation and dimension
      """
      config = request.param
      input0 = tf.keras.Input(
          shape=config["X_train_multi_inputs"][0].shape[1:]
      )
      input1 = tf.keras.Input(
          shape=config["X_train_multi_inputs"][1].shape[1:]
      )
  
      x = tf.keras.layers.Flatten()(input0)
      x = tf.keras.layers.Concatenate()([x, input1])
  
      x = tf.keras.layers.Dense(20, activation="relu")(x)
      outputs = tf.keras.layers.Dense(
          config["output_dim"], activation=config["activation"]
      )(x)
      if config.get("squash_output", False):
          outputs = tf.keras.layers.Reshape(())(outputs)
      model = tf.keras.models.Model(
          inputs=[input0, input1], outputs=outputs
      )
      model.compile(loss=config["loss"], optimizer="adam")
  
      model.fit(
          config["X_train_multi_inputs"],
          config["y_train"],
          epochs=1,
          batch_size=256,
          verbose=0,
      )
  
      return model
  
imports: from functools import partial
  import numpy
  import pytest
  import tensorflow
  from numpy.testing import assert_allclose
  from tensorflow.keras import Model
  from alibi.api.interfaces import Explanation
  from alibi.explainers import IntegratedGradients
  from alibi.explainers.integrated_gradients import _get_target_from_target_fn, _run_forward_from_layer, _run_forward_to_layer, _check_target, _select_target
label_desc: Use image node_id 2 for calling a global function with example usage: ffn_model_multi_inputs(request) and returns: model