class_docstr: Integration tests for the classical fisher information matrix CFIM
class_docstr_tok: ["Integration","tests","for","the","classical","fisher","information","matrix","CFIM"]
docstr: Testing multiple args to be differentiated using torch
docstr_tok: ["Testing","multiple","args","to","be","differentiated","using","torch"]
code: def test_cfim_multiple_args_torch(self):
      """Testing multiple args to be differentiated using torch"""
      import torch
  
      n_wires = 3
  
      dev = qml.device("default.qubit", wires=n_wires)
  
      @qml.qnode(dev)
      def circ(x, y, z):
          for xi in x:
              qml.RX(xi, wires=0)
              qml.RX(xi, wires=1)
          for yi in y:
              qml.RY(yi, wires=0)
              qml.RY(yi, wires=1)
          for zi in z:
              qml.RZ(zi, wires=0)
              qml.RZ(zi, wires=1)
          return qml.probs(wires=range(n_wires))
  
      x = np.pi / 8 * torch.ones(2, requires_grad=True)
      y = np.pi / 8 * torch.ones(10, requires_grad=True)
      z = torch.ones(1, requires_grad=True)
      cfim = qml.qinfo.classical_fisher(circ)(x, y, z)
      assert np.allclose(
          cfim[0].detach().numpy(), 2.0 / 3.0 * np.ones((2, 2))
      )
      assert np.allclose(
          cfim[1].detach().numpy(), 2.0 / 3.0 * np.ones((10, 10))
      )
      assert np.allclose(cfim[2].detach().numpy(), np.zeros((1, 1)))
  
imports: import pytest
  import numpy
  import pennylane
  import pennylane.numpy
  from pennylane.qinfo import classical_fisher, quantum_fisher
  from pennylane.qinfo.transforms import _make_probs, _compute_cfim
label_desc: Use image node_id 8 for calling the TestInterfacesClassicalFisher obj's underlying member method code with example usage: obj.test_cfim_multiple_args_torch() and returns: qml