code: def test_torch_with_sequence_argnum(
      circuit, args, expected_jac, argnum, diff_method, interface
  ):
      r"""Test ``classical_jacobian`` with ``argnum=Sequence[int]`` and Torch."""
      import torch
  
      args = tuple((torch.tensor(arg) for arg in args))
      dev = qml.device("default.qubit", wires=2)
      qnode = qml.QNode(
          circuit, dev, interface=interface, diff_method=diff_method
      )
      jac = classical_jacobian(qnode, argnum=argnum)(*args)
      expected_jac = tuple((expected_jac[num] for num in argnum))
      assert len(jac) == len(expected_jac)
      for _jac, _expected_jac in zip(jac, expected_jac):
          assert np.allclose(_jac, _expected_jac)
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane import numpy
  from pennylane.gradients.classical_jacobian import classical_jacobian
label_desc: Use image node_id 19 for calling a global function with example usage: test_torch_with_sequence_argnum(circuit, args, expected_jac, argnum, diff_method, interface) without return types