class_docstr: Tests for Fidelity function between two QNodes.
docstr: Test the gradient of fidelity between Rx and PauliZ circuits with Torch and two parameters.
code: def test_fidelity_qnodes_rx_pauliz_torch_grad_two_params(
      self, param, wire, interface
  ):
      """Test the gradient of fidelity between Rx and PauliZ circuits with Torch and two parameters."""
      import torch
  
      dev = qml.device("default.qubit", wires=wire)
  
      @qml.qnode(dev, interface=interface, diff_method="backprop")
      def circuit0(x):
          qml.RX(x, wires=0)
          return qml.state()
  
      @qml.qnode(dev, interface=interface, diff_method="backprop")
      def circuit1(x):
          qml.RX(x, wires=0)
          qml.RX(-x, wires=0)
          qml.PauliZ(wires=0)
          return qml.state()
  
      expected_fid_grad = expected_grad_fidelity_rx_pauliz(param)
      param = torch.tensor(
          param, dtype=torch.float64, requires_grad=True
      )
      param2 = torch.tensor(0, dtype=torch.float64, requires_grad=True)
      fid = qml.qinfo.fidelity(
          circuit0, circuit1, wires0=[0], wires1=[0]
      )((param), (param2))
      fid.backward()
      fid_grad = (param.grad, param2.grad)
      assert qml.math.allclose(fid_grad, (expected_fid_grad, 0.0))
  
imports: import pytest
  import pennylane
  from pennylane import numpy
label_desc: Use image node_id 29 for calling the TestFidelityQnode obj's underlying member method code with example usage: obj.test_fidelity_qnodes_rx_pauliz_torch_grad_two_params(param, wire, interface) and returns: qml, qml