class_docstr: Test the torch interface execute function
  integrates well for both forward and backward execution
class_docstr_tok: ["Test","the","torch","interface","execute","function","integrates","well","for","both","forward","and","backward","execution"]
docstr: Test that the torch interface works correctly
  with a matrix parameter
docstr_tok: ["Test","that","the","torch","interface","works","correctly","with","a","matrix","parameter"]
code: def test_matrix_parameter(self, execute_kwargs, device, shots):
      """Test that the torch interface works correctly
      with a matrix parameter"""
      U = torch.tensor(
          [[0, 1], [1, 0]], requires_grad=False, dtype=torch.float64
      )
      a = torch.tensor(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 torch.allclose(
          res, -torch.cos(a), atol=atol_for_shots(shots)
      )
  
      jac = torch.autograd.functional.jacobian(lambda y: cost(y, U), a)
      assert isinstance(jac, torch.Tensor)
      assert torch.allclose(
          jac, torch.sin(a), atol=atol_for_shots(shots), rtol=0
      )
  
imports: import numpy
  import pytest
  from param_shift_dev import ParamShiftDerivativesDevice
  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 TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_matrix_parameter(execute_kwargs, device, shots) and returns: execute