class_docstr: Test the core functionality of the VQE module
class_docstr_tok: ["Test","the","core","functionality","of","the","VQE","module"]
docstr: Test that the gradient of ExpvalCost is accessible and correct when using observable
  optimization and the autograd interface with a zero Hamiltonian.
docstr_tok: ["Test","that","the","gradient","of","ExpvalCost","is","accessible","and","correct","when","using","observable","optimization","and","the","autograd","interface","with","a","zero","Hamiltonian","."]
code: def test_grad_zero_hamiltonian(self, opt):
      """Test that the gradient of ExpvalCost is accessible and correct when using observable
      optimization and the autograd interface with a zero Hamiltonian.
      """
      dev = qml.device("default.qubit", wires=4)
      hamiltonian = qml.Hamiltonian([0], [qml.PauliX(0)])
  
      cost = catch_warn_ExpvalCost(
          qml.templates.StronglyEntanglingLayers,
          hamiltonian,
          dev,
          optimize=opt,
          diff_method="parameter-shift",
      )
  
      np.random.seed(1967)
      shape = qml.templates.StronglyEntanglingLayers.shape(
          n_layers=2, n_wires=4
      )
      w = pnp.random.random(shape, requires_grad=True)
  
      dc = qml.grad(cost)(w)
      assert np.allclose(dc, 0)
  
imports: import copy
  import numpy
  import pytest
  import pennylane
  from pennylane import numpy
label_desc: Use image node_id 12 for calling the TestVQE obj's underlying member method code with example usage: obj.test_grad_zero_hamiltonian(opt) without return types