Module tflite.UnidirectionalSequenceLSTMOptions

def AddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
    UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs)

def AddCellClip(builder, cellClip):
    UnidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip)

def AddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors):
    UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors)

def AddFusedActivationFunction(builder, fusedActivationFunction):
    UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction)

def AddProjClip(builder, projClip):
    UnidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip)

def AddTimeMajor(builder, timeMajor):
    UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor)

def End(builder):
    return UnidirectionalSequenceLSTMOptionsEnd(builder)

def Start(builder):
    UnidirectionalSequenceLSTMOptionsStart(builder)

def UnidirectionalSequenceLSTMOptionsAddAsymmetricQuantizeInputs(builder, asymmetricQuantizeInputs):
    builder.PrependBoolSlot(4, asymmetricQuantizeInputs, 0)

def UnidirectionalSequenceLSTMOptionsAddCellClip(builder, cellClip):
    builder.PrependFloat32Slot(1, cellClip, 0.0)

def UnidirectionalSequenceLSTMOptionsAddDiagonalRecurrentTensors(builder, diagonalRecurrentTensors):
    builder.PrependBoolSlot(5, diagonalRecurrentTensors, 0)

def UnidirectionalSequenceLSTMOptionsAddFusedActivationFunction(builder, fusedActivationFunction):
    builder.PrependInt8Slot(0, fusedActivationFunction, 0)

def UnidirectionalSequenceLSTMOptionsAddProjClip(builder, projClip):
    builder.PrependFloat32Slot(2, projClip, 0.0)

def UnidirectionalSequenceLSTMOptionsAddTimeMajor(builder, timeMajor):
    builder.PrependBoolSlot(3, timeMajor, 0)

def UnidirectionalSequenceLSTMOptionsEnd(builder):
    return builder.EndObject()

def UnidirectionalSequenceLSTMOptionsStart(builder):
    builder.StartObject(6)

class UnidirectionalSequenceLSTMOptions(object):
    __slots__ = ['_tab']

    @classmethod
    def GetRootAs(cls, buf, offset=0):
        n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
        x = UnidirectionalSequenceLSTMOptions()
        x.Init(buf, n + offset)
        return x

    @classmethod
    def GetRootAsUnidirectionalSequenceLSTMOptions(cls, buf, offset=0):
        """This method is deprecated. Please switch to GetRootAs."""
        return cls.GetRootAs(buf, offset)
    @classmethod
    def UnidirectionalSequenceLSTMOptionsBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
        return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x46\x4C\x33", size_prefixed=size_prefixed)

    # UnidirectionalSequenceLSTMOptions
    def Init(self, buf, pos):
        self._tab = flatbuffers.table.Table(buf, pos)

    # UnidirectionalSequenceLSTMOptions
    def FusedActivationFunction(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
        return 0

    # UnidirectionalSequenceLSTMOptions
    def CellClip(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
        return 0.0

    # UnidirectionalSequenceLSTMOptions
    def ProjClip(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
        return 0.0

    # UnidirectionalSequenceLSTMOptions
    def TimeMajor(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
        if o != 0:
            return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
        return False

    # UnidirectionalSequenceLSTMOptions
    def AsymmetricQuantizeInputs(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
        if o != 0:
            return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
        return False

    # UnidirectionalSequenceLSTMOptions
    def DiagonalRecurrentTensors(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
        if o != 0:
            return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
        return False