class_docstr: Tests for Fidelity function between two QNodes.
docstr: Test the gradient of the fidelity between two trainable circuits with Torch.
code: def test_fidelity_qnodes_rx_tworx_torch_grad(
      self, param, wire, interface
  ):
      """Test the gradient of the fidelity between two trainable circuits with Torch."""
      import torch
  
      dev = qml.device("default.qubit", wires=wire)
  
      @qml.qnode(dev, interface=interface, diff_method="backprop")
      def circuit(x):
          qml.RX(x, wires=0)
          return qml.state()
  
      expected = expected_grad_fidelity_rx_pauliz(param)
      expected_fid = [-expected, expected]
      params = (
          torch.tensor(param, dtype=torch.float64, requires_grad=True),
          torch.tensor(
              2 * param, dtype=torch.float64, requires_grad=True
          ),
      )
      fid = qml.qinfo.fidelity(
          circuit, circuit, wires0=[0], wires1=[0]
      )(*params)
      fid.backward()
      fid_grad = [p.grad for p in params]
      assert qml.math.allclose(fid_grad, expected_fid)
  
imports: import pytest
  import pennylane
  from pennylane import numpy
label_desc: Use image node_id 15 for calling the TestFidelityQnode obj's underlying member method code with example usage: obj.test_fidelity_qnodes_rx_tworx_torch_grad(param, wire, interface) and returns: qml