code: def test_saliency_shapes():
      """Test computing saliency maps for multiple outputs with multiple dimensions."""
      inputs = tf.keras.Input(shape=(2, 3))
      flatten = tf.keras.layers.Flatten()(inputs)
      output1 = tf.keras.layers.Reshape((4, 1))(
          tf.keras.layers.Dense(4)(flatten)
      )
      output2 = tf.keras.layers.Reshape((1, 5))(
          tf.keras.layers.Dense(5)(flatten)
      )
      keras_model = tf.keras.Model(
          inputs=inputs, outputs=[output1, output2]
      )
      model = dc.models.KerasModel(
          keras_model, dc.models.losses.L2Loss()
      )
      x = np.random.random((2, 3))
      s = model.compute_saliency(x)
      assert len(s) == 2
      assert s[0].shape == (4, 1, 2, 3)
      assert s[1].shape == (1, 5, 2, 3)
  
file_path: deepchem/deepchem/models/tests/test_kerasmodel.py
imports: import os
  import math
  import deepchem
  import numpy
  import unittest
  import pytest
label_desc: Use image node_id 9 for calling a global function with example usage: test_saliency_shapes() without return types