class_docstr: Test that the transform is differentiable
docstr: Tests that the output of the parameter-shift CV transform
  can be executed using Torch.
docstr_tok: ["Tests","that","the","output","of","the","parameter-shift","CV","transform","can","be","executed","using","Torch","."]
code: def test_torch(self, tol):
      """Tests that the output of the parameter-shift CV transform
      can be executed using Torch."""
      import torch
  
      dev = qml.device("default.gaussian", wires=1)
      params = torch.tensor(
          [0.543, -0.654], dtype=torch.float64, requires_grad=True
      )
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.Squeezing(params[0], 0, wires=0)
          qml.Rotation(params[1], wires=0)
          qml.var(qml.QuadX(wires=[0]))
  
      tape = qml.tape.QuantumScript.from_queue(q)
      tape.trainable_params = {0, 2}
      tapes, fn = qml.gradients.param_shift_cv(tape, dev)
      jac = fn(
          qml.execute(
              tapes,
              dev,
              param_shift_cv,
              gradient_kwargs={"dev": dev},
              interface="torch",
          )
      )
  
      r, phi = params.detach().numpy()
  
      expected = np.array(
          [
              2 * np.exp(2 * r) * np.sin(phi) ** 2
              - 2 * np.exp(-2 * r) * np.cos(phi) ** 2,
              2 * np.sinh(2 * r) * np.sin(2 * phi),
          ]
      )
      assert np.allclose(
          jac[0].detach().numpy(), expected[0], atol=tol, rtol=0
      )
      assert np.allclose(
          jac[1].detach().numpy(), expected[1], atol=tol, rtol=0
      )
  
      cost = jac[1]
      cost.backward()
      hess = params.grad
      expected = np.array(
          [
              4 * np.cosh(2 * r) * np.sin(2 * phi),
              4 * np.cos(2 * phi) * np.sinh(2 * r),
          ]
      )
  
      assert np.allclose(
          hess.detach().numpy(), expected, atol=0.1, rtol=0
      )
  
imports: import unittest.mock
  import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.gradients import param_shift_cv
  from pennylane.gradients.gradient_transform import choose_trainable_params
  from pennylane.gradients.parameter_shift_cv import _grad_method_cv, _find_gradient_methods_cv, _transform_observable
label_desc: Use image node_id 3 for calling the TestParamShiftInterfaces obj's underlying member method code with example usage: obj.test_torch(tol) without return types