tensorflow_quantum/python/layers/high_level/controlled_pqc.py [171:214]:
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        if len(self._symbols_list) == 0:
            raise ValueError("model_circuit has no sympy.Symbols. Please "
                             "provide a circuit that contains symbols so "
                             "that their values can be trained.")

        # Ingest operators.
        if isinstance(operators, (cirq.PauliString, cirq.PauliSum)):
            operators = [operators]

        if not isinstance(operators, (list, np.ndarray, tuple)):
            raise TypeError("operators must be a cirq.PauliSum or "
                            "cirq.PauliString, or a list, tuple, "
                            "or np.array containing them. "
                            "Got {}.".format(type(operators)))
        if not all([
                isinstance(op, (cirq.PauliString, cirq.PauliSum))
                for op in operators
        ]):
            raise TypeError("Each element in operators to measure "
                            "must be a cirq.PauliString"
                            " or cirq.PauliSum")

        self._operators = util.convert_to_tensor([operators])

        # Ingest and promote repetitions.
        self._analytic = False
        if repetitions is None:
            self._analytic = True

        if not self._analytic and not isinstance(repetitions, numbers.Integral):
            raise TypeError("repetitions must be a positive integer value."
                            " Given: ".format(repetitions))

        if not self._analytic and repetitions <= 0:
            raise ValueError("Repetitions must be greater than zero.")

        if not self._analytic:
            self._repetitions = tf.constant(
                [[repetitions for _ in range(len(operators))]],
                dtype=tf.dtypes.int32)

        # Ingest backend and differentiator.
        if backend == 'noisy':
            raise ValueError("noisy backend value is not supported in "
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tensorflow_quantum/python/layers/high_level/pqc.py [191:229]:
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        if len(self._symbols_list) == 0:
            raise ValueError("model_circuit has no sympy.Symbols. Please "
                             "provide a circuit that contains symbols so "
                             "that their values can be trained.")

        # Ingest operators.
        if isinstance(operators, (cirq.PauliString, cirq.PauliSum)):
            operators = [operators]
        if not isinstance(operators, (list, np.ndarray, tuple)):
            raise TypeError("operators must be a cirq.PauliSum or "
                            "cirq.PauliString, or a list, tuple, "
                            "or np.array containing them. "
                            "Got {}.".format(type(operators)))
        if not all([
                isinstance(op, (cirq.PauliString, cirq.PauliSum))
                for op in operators
        ]):
            raise TypeError("Each element in operators to measure "
                            "must be a cirq.PauliString"
                            " or cirq.PauliSum")
        self._operators = util.convert_to_tensor([operators])

        # Ingest and promote repetitions.
        self._analytic = False
        if repetitions is None:
            self._analytic = True
        if not self._analytic and not isinstance(repetitions, numbers.Integral):
            raise TypeError("repetitions must be a positive integer value."
                            " Given: ".format(repetitions))
        if not self._analytic and repetitions <= 0:
            raise ValueError("Repetitions must be greater than zero.")
        if not self._analytic:
            self._repetitions = tf.constant(
                [[repetitions for _ in range(len(operators))]],
                dtype=tf.dtypes.int32)

        # Set backend and differentiator.
        if backend == 'noisy':
            raise ValueError("noisy backend value is not supported in "
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