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 PyTorch
docstr_tok: ["Test","that","a","gradient","transform","remains","differentiable","with","PyTorch"]
code: def test_torch(self, tol):
      """Test that a gradient transform remains differentiable
      with PyTorch"""
      import torch
  
      dev = qml.device("default.qubit", wires=2)
  
      @qml.gradients.param_shift
      @qml.qnode(dev, interface="torch")
      def circuit(x):
          qml.RY(x, wires=[1])
          qml.CNOT(wires=[0, 1])
          return qml.var(qml.PauliX(1))
  
      x_ = -0.654
      x = torch.tensor(x_, dtype=torch.float64, requires_grad=True)
      res = circuit(x)
  
      expected = -2 * np.cos(x_) * np.sin(x_)
      assert np.allclose(res.detach(), expected, atol=tol, rtol=0)
  
      res.backward()
      expected = -2 * np.cos(2 * x_)
      assert np.allclose(x.grad.detach(), 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 3 for calling the TestInterfaceIntegration obj's underlying member method code with example usage: obj.test_torch(tol) and returns: qml