class_docstr: Test that the measure_with_samples function works as expected for
  Hamiltonian and Sum observables
class_docstr_tok: ["Test","that","the","measure_with_samples","function","works","as","expected","for","Hamiltonian","and","Sum","observables"]
docstr: Test that sampling works well for Hamiltonian observables
docstr_tok: ["Test","that","sampling","works","well","for","Hamiltonian","observables"]
code: def test_hamiltonian_expval(self):
      """Test that sampling works well for Hamiltonian observables"""
      x, y = np.array(0.67), np.array(0.95)
      ops = [qml.RY(x, wires=0), qml.RZ(y, wires=0)]
      meas = [
          qml.expval(
              qml.Hamiltonian(
                  [0.8, 0.5], [qml.PauliZ(0), qml.PauliX(0)]
              )
          )
      ]
  
      qs = qml.tape.QuantumScript(ops, meas, shots=10000)
      res = simulate(qs, rng=200)
  
      expected = 0.8 * np.cos(x) + 0.5 * np.real(
          np.exp(y * 1j)
      ) * np.sin(x)
      assert np.allclose(res, expected, atol=0.01)
  
imports: from random import shuffle
  import pytest
  import numpy
  import pennylane
  from pennylane.devices.qubit import simulate
  from pennylane.devices.qubit.simulate import _FlexShots
  from pennylane.devices.qubit import sample_state, measure_with_samples
  from pennylane.devices.qubit.sampling import _sample_state_jax
  from pennylane.measurements import Shots
label_desc: Use image node_id 1 for calling the TestHamiltonianSamples obj's underlying member method code with example usage: obj.test_hamiltonian_expval() without return types