class_docstr: To run: "python -m unittest saliency.core.guided_ig_test" from the top-level directory.
docstr: Returns gradient of a linear function 2 * x1 + x2.
  
  Guided IG for linear function produces tha same attribution as Integrated
  Gradients regardless of the parameters.
docstr_tok: ["Returns","gradient","of","a","linear","function","2","*","x1","+","x2",".","Guided","IG","for","linear","function","produces","tha","same","attribution","as","Integrated","Gradients","regardless","of","the","parameters","."]
code: def _linear_test_func(
      self, x_value_batch, call_model_args, expected_keys
  ):
      """Returns gradient of a linear function 2 * x1 + x2.
  
      Guided IG for linear function produces tha same attribution as Integrated
      Gradients regardless of the parameters.
      """
      self.assertListEqual(expected_keys, [INPUT_OUTPUT_GRADIENTS])
      self.assertEqual(call_model_args, "airplane")
      return {INPUT_OUTPUT_GRADIENTS: [[2.0, 1.0]]}
  
file_path: saliency/saliency/core/guided_ig_test.py
imports: import unittest
  from .None import guided_ig
  from .base import INPUT_OUTPUT_GRADIENTS
  import numpy
label_desc: Use image node_id 1 for calling the GuidedIGTest obj's underlying member method code with example usage: obj._linear_test_func(x_value_batch, call_model_args, expected_keys) and returns: dict