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 Sum observables with a shot vector.
docstr_tok: ["Test","that","sampling","works","well","for","Sum","observables","with","a","shot","vector","."]
code: def test_sum_expval_shot_vector(self):
      """Test that sampling works well for Sum observables with a shot vector."""
      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.s_prod(0.8, qml.PauliZ(0))
              + qml.s_prod(0.5, qml.PauliX(0))
          )
      ]
  
      qs = qml.tape.QuantumScript(ops, meas, shots=(10000, 10000))
      res = simulate(qs, rng=200)
  
      expected = 0.8 * np.cos(x) + 0.5 * np.real(
          np.exp(y * 1j)
      ) * np.sin(x)
  
      assert len(res) == 2
      assert isinstance(res, tuple)
      assert np.allclose(res[0], expected, atol=0.01)
      assert np.allclose(res[1], 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 4 for calling the TestHamiltonianSamples obj's underlying member method code with example usage: obj.test_sum_expval_shot_vector() without return types