code: def test_torch(self, v):
      import torch
  
      def circuit(theta):
          qml.RX(theta, wires=0)
          qml.PauliZ(wires=0)
          qml.CNOT(wires=[0, 1])
  
      def loss(theta):
          U = qml.matrix(circuit)(theta)
          return qml.math.real(qml.math.trace(U))
  
      x = torch.tensor(v, requires_grad=True)
      l = loss(x)
      l.backward()
      dl = x.grad
      matrix = qml.matrix(circuit)(x)
  
      assert isinstance(matrix, torch.Tensor)
      assert np.allclose(l.detach(), 2 * np.cos(v / 2))
      assert np.allclose(dl.detach(), -np.sin(v / 2))
  
imports: from functools import reduce, partial
  from warnings import catch_warnings
  import pytest
  from gate_data import I, X, Y, Z, H, S, CNOT, Rotx, Roty
  import pennylane
  from pennylane import numpy
  from pennylane.transforms.op_transforms import OperationTransformError
label_desc: Use image node_id 2 for calling the TestDifferentiation obj's underlying member method code with example usage: obj.test_torch(v) and returns: qml