class_docstr: Tests that the transforms are differentiable if the inner execution is differentiable.
  
  Note that testing is only done for the required method for each ml framework.
class_docstr_tok: ["Tests","that","the","transforms","are","differentiable","if","the","inner","execution","is","differentiable",".","Note","that","testing","is","only","done","for","the","required","method","for","each","ml","framework","."]
docstr: Test that the derivative of compute_vjp can be taken with torch.
docstr_tok: ["Test","that","the","derivative","of","compute_vjp","can","be","taken","with","torch","."]
code: def test_vjp_torch(self):
      """Test that the derivative of compute_vjp can be taken with torch."""
  
      import torch
  
      jpc = param_shift_jpc
  
      def f(x, dy):
          tape = qml.tape.QuantumScript(
              [qml.RX(x, 0)], [qml.expval(qml.PauliZ(0))]
          )
          vjp = jpc.compute_vjp((tape,), (dy,))
          return vjp[0]
  
      x = torch.tensor(0.1, requires_grad=True)
      dy = torch.tensor(2.0, requires_grad=True)
  
      res = f(x, dy)
      assert qml.math.allclose(res, -2.0 * np.sin(0.1))
  
      res.backward()
      assert qml.math.allclose(x.grad, -2.0 * np.cos(0.1))
      assert qml.math.allclose(dy.grad, -np.sin(0.1))
  
imports: import pytest
  from cachetools import LRUCache
  from param_shift_dev import ParamShiftDerivativesDevice
  import numpy
  import pennylane
  from pennylane.workflow.jacobian_products import JacobianProductCalculator, TransformJacobianProducts, DeviceDerivatives, DeviceJacobianProducts, LightningVJPs
label_desc: Use image node_id 3 for calling the TestTransformsDifferentiability obj's underlying member method code with example usage: obj.test_vjp_torch() and returns: vjp