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
code: def test_matrix_parameter(self, torch_device, U, execute_kwargs, tol):
      """Test that the torch interface works correctly
      with a matrix parameter"""
      a_val = 0.1
      a = torch.tensor(a_val, requires_grad=True, device=torch_device)
  
      if isinstance(U, torch.Tensor) and torch_device is not None:
          U = U.to(torch_device)
  
      dev = qml.device("default.qubit.legacy", wires=2)
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.QubitUnitary(U, wires=0)
          qml.RY(a, wires=0)
          qml.expval(qml.PauliZ(0))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      res = execute([tape], dev, **execute_kwargs)[0]
      assert tape.trainable_params == [1]
  
      expected = torch.tensor(
          -np.cos(a_val), dtype=res.dtype, device=torch_device
      )
      assert torch.allclose(res.detach(), expected, atol=tol, rtol=0)
  
      res.backward()
      expected = torch.tensor(
          [np.sin(a_val)], dtype=a.grad.dtype, device=torch_device
      )
      assert torch.allclose(a.grad, expected, atol=tol, rtol=0)
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane.gradients import finite_diff, param_shift
  from pennylane import execute
label_desc: Use image node_id 8 for calling the TestTorchExecuteIntegration obj's underlying member method code with example usage: obj.test_matrix_parameter(torch_device, U, execute_kwargs, tol) without return types