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path: root/tools/tflitefile_tool/tflite/Tensor.py
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# automatically generated by the FlatBuffers compiler, do not modify

# namespace: tflite

import flatbuffers


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

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

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

    # Tensor
    def Shape(self, j):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        if o != 0:
            a = self._tab.Vector(o)
            return self._tab.Get(
                flatbuffers.number_types.Int32Flags,
                a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4))
        return 0

    # Tensor
    def ShapeAsNumpy(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        if o != 0:
            return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o)
        return 0

    # Tensor
    def ShapeLength(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        if o != 0:
            return self._tab.VectorLen(o)
        return 0

    # Tensor
    def Type(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Int8Flags, o + self._tab.Pos)
        return 0

    # Tensor
    def Buffer(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos)
        return 0

    # Tensor
    def Name(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
        if o != 0:
            return self._tab.String(o + self._tab.Pos)
        return None

    # Tensor
    def Quantization(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
        if o != 0:
            x = self._tab.Indirect(o + self._tab.Pos)
            from .QuantizationParameters import QuantizationParameters
            obj = QuantizationParameters()
            obj.Init(self._tab.Bytes, x)
            return obj
        return None

    # Tensor
    def IsVariable(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


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


def TensorAddShape(builder, shape):
    builder.PrependUOffsetTRelativeSlot(
        0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0)


def TensorStartShapeVector(builder, numElems):
    return builder.StartVector(4, numElems, 4)


def TensorAddType(builder, typeSlot):
    builder.PrependInt8Slot(1, typeSlot, 0)


def TensorAddBuffer(builder, buffer):
    builder.PrependUint32Slot(2, buffer, 0)


def TensorAddName(builder, name):
    builder.PrependUOffsetTRelativeSlot(
        3, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0)


def TensorAddQuantization(builder, quantization):
    builder.PrependUOffsetTRelativeSlot(
        4, flatbuffers.number_types.UOffsetTFlags.py_type(quantization), 0)


def TensorAddIsVariable(builder, isVariable):
    builder.PrependBoolSlot(5, isVariable, 0)


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