code: def test_integrated_gradients_binary_classification_single_output_multi_inputs(
      ffn_model_multi_inputs,
      method,
      baselines,
      target_fn,
      target,
      recwarn,
  ):
      model = ffn_model_multi_inputs
      ig = IntegratedGradients(
          model, target_fn=target_fn, n_steps=50, method=method
      )
  
      explanations = ig.explain(
          X_test_multi_inputs, baselines=baselines, target=target
      )
  
      # check for warning if target_fn != None
      if target_fn:
          assert len(recwarn) == 1
  
      assert isinstance(explanations, Explanation)
      assert max([len(x) for x in X_test_multi_inputs]) == min(
          [len(x) for x in X_test_multi_inputs]
      )
      assert max(
          [len(x) for x in explanations["data"]["attributions"]]
      ) == min([len(x) for x in explanations["data"]["attributions"]])
      assert len(explanations["data"]["attributions"][0]) == N_TEST
      assert len(X_test_multi_inputs[0]) == N_TEST
  
      attrs = explanations["data"]["attributions"]
      for i in range(len(attrs)):
          assert attrs[i].shape == X_test_multi_inputs[i].shape
  
      assert "deltas" in explanations["data"].keys()
      assert explanations["data"]["deltas"].shape[0] == N_TEST
  
      assert "predictions" in explanations["data"].keys()
      assert explanations["data"]["predictions"].shape[0] == N_TEST
  
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 11 for calling a global function with example usage: test_integrated_gradients_binary_classification_single_output_multi_inputs(ffn_model_multi_inputs, method, baselines, target_fn, target, recwarn) without return types