code: def test_torch_not_trainable_only(diff_method, interface):
      r"""Test ``classical_jacobian`` with ``argnum=<int>`` and Torch
      with ``trainable_only=False`` ."""
      import torch
  
      dev = qml.device("default.qubit", wires=2)
      qnode = qml.QNode(
          circuit_0, dev, interface=interface, diff_method=diff_method
      )
      jac = classical_jacobian(qnode, argnum=0, trainable_only=False)(
          torch.tensor(a, requires_grad=True)
      )
      assert np.allclose(jac, expected_jac_not_trainable_only)
  
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 22 for calling a global function with example usage: test_torch_not_trainable_only(diff_method, interface) without return types