class_docstr: To run: "python -m saliency.core.integrated_gradients_test" from the top-level directory.
code: def setUp(self):
      super().setUp()
  
      def y_fn(arr):
          return np.array([5 * arr[0], arr[1] * arr[1], np.sin(arr[2])])
  
      # Calculate the value of `y` at the baseline. `y` is returned as multiple
      # numbers, with the assumption that in a real network these values would be
      # summed to a single output number.
      self.x_baseline_val = np.array([0.5, 1.0, 1.0], dtype=float)
      y_baseline_val = y_fn(self.x_baseline_val)
  
      # Calculate the value of `y` at the input.
      self.x_input_val = np.array([1.0, 2.0, 3.0], dtype=float)
      y_input_val = y_fn(self.x_input_val)
  
      # Because each variable is independent, the expected contribution is equal
      # to the difference between the baseline and input contribution for each.
      self.expected_val = y_input_val - y_baseline_val
  
      self.ig_instance = integrated_gradients.IntegratedGradients()
  
file_path: saliency/saliency/core/integrated_gradients_test.py
imports: import unittest
  import unittest.mock
  from .base import INPUT_OUTPUT_GRADIENTS
  from .base import SHAPE_ERROR_MESSAGE
  from .None import integrated_gradients
  import numpy
label_desc: Use image node_id 1 for calling the IntegratedGradientsTest obj's underlying member method code with example usage: obj.setUp() and returns: np