tensorflow_quantum/python/layers/high_level/noisy_controlled_pqc.py [179:238]:
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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.
        if repetitions is None:
            raise ValueError("Value for repetitions must be provided when "
                             "using noisy simulation.")
        if not isinstance(repetitions, numbers.Integral):
            raise TypeError("repetitions must be a positive integer value."
                            " Given: ".format(repetitions))
        if repetitions <= 0:
            raise ValueError("Repetitions must be greater than zero.")

        self._repetitions = tf.constant(
            [[repetitions for _ in range(len(operators))]],
            dtype=tf.dtypes.int32)

        # Ingest differentiator.
        if differentiator is None:
            differentiator = parameter_shift.ParameterShift()

        # Ingest and promote sample based.
        if sample_based is None:
            raise ValueError("Please specify sample_based=False for analytic "
                             "calculations based on monte-carlo trajectories,"
                             " or sampled_based=True for measurement based "
                             "noisy estimates.")
        if not isinstance(sample_based, bool):
            raise TypeError("sample_based must be either True or False."
                            " received: {}".format(type(sample_based)))

        if not sample_based:
            self._executor = differentiator.generate_differentiable_op(
                sampled_op=noisy_expectation_op.expectation)
        else:
            self._executor = differentiator.generate_differentiable_op(
                sampled_op=noisy_sampled_expectation_op.sampled_expectation)

        self._append_layer = elementary.AddCircuit()
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tensorflow_quantum/python/layers/high_level/noisy_pqc.py [187:244]:
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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.
        if repetitions is None:
            raise ValueError("Value for repetitions must be provided when "
                             "using noisy simulation.")
        if not isinstance(repetitions, numbers.Integral):
            raise TypeError("repetitions must be a positive integer value."
                            " Given: ".format(repetitions))
        if repetitions <= 0:
            raise ValueError("Repetitions must be greater than zero.")

        self._repetitions = tf.constant(
            [[repetitions for _ in range(len(operators))]],
            dtype=tf.dtypes.int32)

        # Ingest differentiator.
        if differentiator is None:
            differentiator = parameter_shift.ParameterShift()

        # Ingest and promote sample based.
        if sample_based is None:
            raise ValueError("Please specify sample_based=False for analytic "
                             "calculations based on monte-carlo trajectories,"
                             " or sampled_based=True for measurement based "
                             "noisy estimates.")
        if not isinstance(sample_based, bool):
            raise TypeError("sample_based must be either True or False."
                            " received: {}".format(type(sample_based)))

        if not sample_based:
            self._executor = differentiator.generate_differentiable_op(
                sampled_op=noisy_expectation_op.expectation)
        else:
            self._executor = differentiator.generate_differentiable_op(
                sampled_op=noisy_sampled_expectation_op.sampled_expectation)

        self._append_layer = elementary.AddCircuit()
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