tensorflow_quantum/python/layers/high_level/controlled_pqc.py [131:193]:
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                 differentiator=None,
                 **kwargs):
        """Instantiate this layer.

        Create a layer that will output expectation values of the given
        operators when fed quantum data to it's input layer. This layer will
        take two input tensors, one representing a quantum data source (these
        circuits must not contain any symbols) and the other representing
        control parameters for the model circuit that gets appended to the
        datapoints.

        model_circuit: `cirq.Circuit` containing `sympy.Symbols` that will be
            used as the model which will be fed quantum data inputs.
        operators: `cirq.PauliSum` or Python `list` of `cirq.PauliSum` objects
            used as observables at the end of the model circuit.
        repetitions: Optional Python `int` indicating how many samples to use
            when estimating expectation values. If `None` analytic expectation
            calculation is used.
        backend: Optional Backend to use to simulate states. Defaults to
            the noiseless TensorFlow simulator, however users may also
            specify a preconfigured cirq simulation object to use instead.
            If a cirq object is given it must inherit `cirq.Sampler` if
            `sampled_based` is True or it must inherit
            `cirq.sim.simulator.SimulatesExpectationValues` if `sample_based` is
            False.
        differentiator: Optional `tfq.differentiator` object to specify how
            gradients of `model_circuit` should be calculated.
        """
        super().__init__(**kwargs)
        # Ingest model_circuit.
        if not isinstance(model_circuit, cirq.Circuit):
            raise TypeError("model_circuit must be a cirq.Circuit object."
                            " Given: ".format(model_circuit))

        self._symbols_list = list(
            sorted(util.get_circuit_symbols(model_circuit)))
        self._symbols = tf.constant([str(x) for x in self._symbols_list])

        self._circuit = util.convert_to_tensor([model_circuit])

        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])
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tensorflow_quantum/python/layers/high_level/noisy_controlled_pqc.py [144:201]:
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                 differentiator=None,
                 **kwargs):
        """Instantiate this layer.

        Create a layer that will output noisy expectation values of the given
        operators when fed quantum data to it's input layer. This layer will
        take two input tensors, one representing a quantum data source (these
        circuits must not contain any symbols) and the other representing
        control parameters for the model circuit that gets appended to the
        datapoints.

        model_circuit: `cirq.Circuit` containing `sympy.Symbols` that will be
            used as the model which will be fed quantum data inputs.
        operators: `cirq.PauliSum` or Python `list` of `cirq.PauliSum` objects
            used as observables at the end of the model circuit.
        repetitions: Python `int` indicating how many trajectories to use
            when estimating expectation values.
        sample_based: Python `bool` indicating whether to use sampling to
            estimate expectations or analytic calculations with each
            trajectory.
        differentiator: Optional `tfq.differentiator` object to specify how
            gradients of `model_circuit` should be calculated.
        """
        super().__init__(**kwargs)
        # Ingest model_circuit.
        if not isinstance(model_circuit, cirq.Circuit):
            raise TypeError("model_circuit must be a cirq.Circuit object."
                            " Given: ".format(model_circuit))

        self._symbols_list = list(
            sorted(util.get_circuit_symbols(model_circuit)))
        self._symbols = tf.constant([str(x) for x in self._symbols_list])

        self._circuit = util.convert_to_tensor([model_circuit])

        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])
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