class_docstr: Test that the gradients for qutrit parametrized operations are correct
docstr: Test that parametrized operations are differentiable with Torch and the gradient is correct
code: def test_differentiability_torch(
      self, op, obs, grad_fn, phi, diff_method, tol
  ):
      """Test that parametrized operations are differentiable with Torch and the gradient is correct"""
      import torch
  
      dev = qml.device("default.qutrit", wires=1)
  
      @qml.qnode(dev, diff_method=diff_method)
      def circuit(phi):
          if op is qml.TRZ:
              # Without Hadamard the derivative is always 0
              qml.THadamard(wires=0, subspace=(0, 1))
          op(phi, wires=0)
          return qml.expval(obs)
  
      phi_torch = torch.tensor(
          phi, requires_grad=True, dtype=torch.float64
      )
      grad = torch.autograd.grad(circuit(phi_torch), phi_torch)
  
      assert qml.math.isclose(grad, grad_fn(phi), atol=tol, rtol=0)
  
imports: import copy
  from functools import reduce
  import pytest
  import numpy
  from gate_data import TSHIFT, TCLOCK
  from pennylane import numpy
  import pennylane
  from pennylane.wires import Wires
label_desc: Use image node_id 5 for calling the TestGrad obj's underlying member method code with example usage: obj.test_differentiability_torch(op, obs, grad_fn, phi, diff_method, tol) and returns: qml