def call()

in tensorflow_quantum/python/layers/circuit_construction/elementary.py [0:0]

• 33 lines of code
• 15 McCabe index (conditional complexity)

    def call(self, inputs, *, append=None, prepend=None):
        """Keras call method.

        Input options:

            1. `inputs` can be a single `cirq.Circuit`, a Python `list` of
                `cirq.Circuit`s or a pre-converted `tf.Tensor` of
                `cirq.Circuit`s.

            2. `append` can be a Python `list` of `cirq.Circuit`s or a
                pre-converted `tf.Tensor` of type `str` (containing circuits).

            3. `prepend` can be a Python `list` of `cirq.Circuit`s or a
                pre-converted `tf.Tensor` of type `str` (containing circuits).

        Output shape:
            `tf.Tensor` of shape [input size] containing circuits with append
            circuits appended or prepend circuits prepended.

        """
        # inputs is circuit.

        if append is None and prepend is None:
            raise ValueError("Values must be provided for append or prepend.")

        if append is not None and prepend is not None:
            raise ValueError(
                "Values cannot be given for both append and prepend.")

        # Ingest input circuit(s).
        if isinstance(inputs, cirq.Circuit):
            inputs = util.convert_to_tensor([inputs])

        if isinstance(inputs, (tuple, list, np.ndarray)):
            inputs = util.convert_to_tensor(inputs)

        if not tf.is_tensor(inputs):
            raise TypeError("Circuits cannot be parsed with given input:"
                            " ".format(inputs))

        batch_dim = tf.gather(tf.shape(inputs), 0)

        # Ingest append circuit(s):
        if append is not None:
            if isinstance(append, cirq.Circuit):
                append = tf.tile(util.convert_to_tensor([append]), [batch_dim])
            if isinstance(append, (tuple, list, np.ndarray)):
                append = util.convert_to_tensor(append)
            if not tf.is_tensor(append):
                raise TypeError(
                    "Append circuits cannot be parsed with given input:"
                    " ".format(append))

            return tfq_utility_ops.append_circuit(inputs, append)

        # Otherwise ingest prepend circuits.
        if isinstance(prepend, cirq.Circuit):
            prepend = tf.tile(util.convert_to_tensor([prepend]), [batch_dim])
        if isinstance(prepend, (tuple, list, np.ndarray)):
            prepend = util.convert_to_tensor(prepend)
        if not tf.is_tensor(prepend):
            raise TypeError(
                "Prepend circuits cannot be parsed with given input:"
                " ".format(prepend))

        return tfq_utility_ops.append_circuit(prepend, inputs)