class_docstr: Test the autograd interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","autograd","interface","execute","function","integrates","well","for","both","forward","and","backward","execution"]
docstr: Test that the autograd interface works correctly
  with a matrix parameter
docstr_tok: ["Test","that","the","autograd","interface","works","correctly","with","a","matrix","parameter"]
code: def test_matrix_parameter(self, execute_kwargs, device, shots):
      """Test that the autograd interface works correctly
      with a matrix parameter"""
      U = np.array([[0, 1], [1, 0]], requires_grad=False)
      a = np.array(0.1, requires_grad=True)
  
      def cost(a, U):
          ops = [qml.QubitUnitary(U, wires=0), qml.RY(a, wires=0)]
          tape = qml.tape.QuantumScript(
              ops, [qml.expval(qml.PauliZ(0))]
          )
          return execute([tape], device, **execute_kwargs)[0]
  
      res = cost(a, U)
      assert np.allclose(res, -np.cos(a), atol=atol_for_shots(shots))
  
      jac_fn = qml.jacobian(cost)
      jac = jac_fn(a, U)
      assert isinstance(jac, np.ndarray)
      assert np.allclose(
          jac, np.sin(a), atol=atol_for_shots(shots), rtol=0
      )
  
imports: import autograd
  import pytest
  from param_shift_dev import ParamShiftDerivativesDevice
  from pennylane import numpy
  import pennylane
  from pennylane.devices import DefaultQubit
  from pennylane.gradients import param_shift
  from pennylane import execute
  from pennylane.measurements import Shots
label_desc: Use image node_id 9 for calling the TestAutogradExecuteIntegration obj's underlying member method code with example usage: obj.test_matrix_parameter(execute_kwargs, device, shots) and returns: execute