class_docstr: Test that the gradient transforms are differentiable
  using each interface
class_docstr_tok: ["Test","that","the","gradient","transforms","are","differentiable","using","each","interface"]
docstr: Test that a gradient transform remains differentiable
  with autograd
docstr_tok: ["Test","that","a","gradient","transform","remains","differentiable","with","autograd"]
code: def test_autograd(self, tol):
      """Test that a gradient transform remains differentiable
      with autograd"""
      dev = qml.device("default.qubit", wires=2)
  
      @qml.gradients.param_shift
      @qml.qnode(dev)
      def circuit(x):
          qml.RY(x**2, wires=[1])
          qml.CNOT(wires=[0, 1])
          return qml.var(qml.PauliX(1))
  
      x = np.array(-0.654, requires_grad=True)
  
      res = circuit(x)
      expected = -4 * x * np.cos(x**2) * np.sin(x**2)
      assert np.allclose(res, expected, atol=tol, rtol=0)
  
      res = qml.grad(circuit)(x)
      expected = -2 * (
          4 * x**2 * np.cos(2 * x**2) + np.sin(2 * x**2)
      )
      assert np.allclose(res, expected, atol=tol, rtol=0)
  
imports: import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.gradients.gradient_transform import _find_gradient_methods, _validate_gradient_methods, choose_trainable_params
label_desc: Use image node_id 1 for calling the TestInterfaceIntegration obj's underlying member method code with example usage: obj.test_autograd(tol) and returns: qml