class_docstr: Unit tests for torch execution
class_docstr_tok: ["Unit","tests","for","torch","execution"]
docstr: Test that no grad on execution uses the `device.batch_execute` and `device.gradients` pathway
code: def test_no_grad_on_execution(self, interface, mocker):
      """Test that no grad on execution uses the `device.batch_execute` and `device.gradients` pathway"""
      dev = qml.device("default.qubit.legacy", wires=1)
      spy_execute = mocker.spy(
          qml.devices.DefaultQubitLegacy, "batch_execute"
      )
      spy_gradients = mocker.spy(
          qml.devices.DefaultQubitLegacy, "gradients"
      )
  
      a = torch.tensor([0.1, 0.2], requires_grad=True)
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.RY(a[0], wires=0)
          qml.RX(a[1], wires=0)
          qml.expval(qml.PauliZ(0))
  
      tape = qml.tape.QuantumScript.from_queue(q)
  
      res = execute(
          [tape],
          dev,
          gradient_fn="device",
          grad_on_execution=False,
          gradient_kwargs={"method": "adjoint_jacobian"},
          interface=interface,
      )[0]
  
      assert dev.num_executions == 1
      spy_execute.assert_called()
      spy_gradients.assert_not_called()
  
      res.backward()
      spy_gradients.assert_called()
  
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 5 for calling the TestTorchExecuteUnitTests obj's underlying member method code with example usage: obj.test_no_grad_on_execution(interface, mocker) without return types