SampledExpectation layer seems to recompute the same circuits for different measurement ops

Example

c = cirq.Circuit()
qubits = cirq.GridQubit.rect(2, 1)
c.append(cirq.rz(0.)(qubits[0]))
c.append(cirq.H(qubits[1]))
sampled_expectation_layer = tfq.layers.SampledExpectation()
output = sampled_expectation_layer(
    [c],
    operators=[cirq.Z(qubits[0]), cirq.Z(qubits[1]), cirq.Z(qubits[0]) * cirq.Z(qubits[1])],
    repetitions=1)
print(output) # we have [1, -1, 1] sometimes
assert output[0,0]*output[0,1] == output[0,2] # and this may fail

It is unnatural to me that, when repetitions=1 (1 sample), we have inconsistent measurement results between Z1, Z2 and Z1*Z2.

And from code here https://github.com/tensorflow/quantum/blob/db2eac4e598f614a8c0e157c56313a23ef1943fb/tensorflow_quantum/core/ops/batch_util.py#L554-L561, this may be due to that each circuit, batch, measurement ops combination is simulated separately on backends. If it is so, it is not only unnatural but also inefficient since one can measure all ops for one circuit simulation.

My understanding may be wrong somewhere, any ideas on this?