class_docstr: Test the core functionality of the VQE module
class_docstr_tok: ["Test","the","core","functionality","of","the","VQE","module"]
docstr: Test that an ExpvalCost with observable optimization gives the same
  result as another ExpvalCost without observable optimization even when there
  are non-unique Hamiltonian terms.
docstr_tok: ["Test","that","an","ExpvalCost","with","observable","optimization","gives","the","same","result","as","another","ExpvalCost","without","observable","optimization","even","when","there","are","non-unique","Hamiltonian","terms","."]
code: def test_optimize_multiple_terms_torch(self):
      """Test that an ExpvalCost with observable optimization gives the same
      result as another ExpvalCost without observable optimization even when there
      are non-unique Hamiltonian terms."""
  
      dev = qml.device("default.qubit", wires=5)
      obs = [
          qml.PauliZ(wires=[2])
          @ qml.PauliZ(wires=[4]),  # <---- These two terms
          qml.PauliZ(wires=[4])
          @ qml.PauliZ(wires=[2]),  # <---- are equal
          qml.PauliZ(wires=[1]),
          qml.PauliZ(wires=[2]),
          qml.PauliZ(wires=[1]) @ qml.PauliZ(wires=[2]),
          qml.PauliZ(wires=[2]) @ qml.PauliZ(wires=[0]),
          qml.PauliZ(wires=[3]) @ qml.PauliZ(wires=[1]),
          qml.PauliZ(wires=[4]) @ qml.PauliZ(wires=[3]),
      ]
  
      coefs = (np.random.rand(len(obs)) - 0.5) * 2
      hamiltonian1 = qml.Hamiltonian(coefs, obs)
      hamiltonian2 = qml.Hamiltonian(coefs, obs)
  
      cost = catch_warn_ExpvalCost(
          qml.templates.StronglyEntanglingLayers,
          hamiltonian1,
          dev,
          optimize=True,
          interface="torch",
          diff_method="parameter-shift",
      )
      cost2 = catch_warn_ExpvalCost(
          qml.templates.StronglyEntanglingLayers,
          hamiltonian2,
          dev,
          optimize=False,
          interface="torch",
          diff_method="parameter-shift",
      )
  
      np.random.seed(1967)
      shape = qml.templates.StronglyEntanglingLayers.shape(
          n_layers=2, n_wires=5
      )
      w = np.random.random(shape)
  
      with qml.Tracker(dev) as tracker:
          c1 = cost(w)
      exec_opt = tracker.totals["executions"]
  
      with tracker:
          c2 = cost2(w)
      exec_no_opt = tracker.totals["executions"]
  
      # was 1, 8 on old device
      assert exec_opt == 1  # Number of groups in the Hamiltonian
      assert exec_no_opt == 8
  
      assert np.allclose(c1, c2)
  
imports: import copy
  import numpy
  import pytest
  import pennylane
  from pennylane import numpy
label_desc: Use image node_id 9 for calling the TestVQE obj's underlying member method code with example usage: obj.test_optimize_multiple_terms_torch() without return types