def Cast(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
inputsRef = curNode.getInputsRef()
assert len(inputsRef) == 1
sourceType = curNode.getAttrMapRef()["SrcT"].getDataType()
destType = curNode.getAttrMapRef()["DstT"].getDataType()
return (
None,
{
curNode.getName():
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Cast.name,
[
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(sourceType.name),
AST.ID(destType.name),
],
)
},
)
def Const(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
assert(len(curNode.getInputsRef()) == 0)
tensor = curNode.getAttrMapRef()["value"].getTensor()
curNodeDataType = curNode.getAttrMapRef()["dtype"].getDataType()
curNodeShape = tensor.getShapeRef()[:] #create a different copy to not change the original copy
tensorConstantVal = tensor.getConstantVal()
if tensorConstantVal is not None:
# Use uinterpreted call of CreateTensor to create the tensor and fill it with a constant value
dataPassed = None
if curNodeDataType == Graph.DataTypeEnum.DT_INT32:
dataPassed = AST.Int(tensorConstantVal, 32, isSecret=False)
elif curNodeDataType == Graph.DataTypeEnum.DT_FLOAT:
dataPassed = AST.Float(tensorConstantVal, isSecret=False)
else:
assert False
if (len(curNodeShape) == 0):
# This is a constant element
retAST = dataPassed
else:
retAST = AST.UninterpFuncCall(curNodeShape,
TFNodesAST.UninterpFuncCallNames.CreateTensor.name,
[dataPassed],
isSecret=False)
else:
# The tensor content is given as byte array. Extract val array from the byte array and create ast.
if curNodeDataType == Graph.DataTypeEnum.DT_INT32:
dataPassed = list(map(lambda x: AST.Int(x, 32, isSecret=False), tensor.getContentAsValArr()[:]))
elif curNodeDataType == Graph.DataTypeEnum.DT_FLOAT:
dataPassed = list(map(lambda x: AST.Float(x, isSecret=False), tensor.getContentAsValArr()[:]))
else:
assert False
retAST = AST.Decl(curNodeShape, None, dataPassed, isSecret=False)
return (None, { curNode.getName() : retAST})
def helper_processPool(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
typeOfPool: str,
):
inputsRef = curNode.getInputsRef()
assert len(inputsRef) == 1
options = {}
stridesUsed = curNode.getAttrMapRef()["strides"].getList().getILi()
assert (stridesUsed[0] == 1) and (stridesUsed[3] == 1)
strideH = stridesUsed[1]
strideW = stridesUsed[2]
kSizeUsed = curNode.getAttrMapRef()["ksize"].getList().getILi()
assert (kSizeUsed[0] == 1) and (kSizeUsed[3] == 1)
kSizeH = kSizeUsed[1]
kSizeW = kSizeUsed[2]
inputShape = extraNodeInfoDict[inputsRef[0]][0]
imgH = inputShape[1]
imgW = inputShape[2]
paddingUsedStr = curNode.getAttrMapRef()["padding"].getS()
[zPadHLeft, zPadHRight, zPadWLeft,
zPadWRight] = TFNodesAST.helper_findPadding(imgH, imgW, kSizeH,
kSizeW, strideH, strideW,
paddingUsedStr)
poolType = None
if typeOfPool == "MAXPOOL":
poolType = AST.Pool.PoolType.MaxPool
elif typeOfPool == "AVGPOOL":
poolType = AST.Pool.PoolType.AvgPool
else:
print("Unknown type of pooling layer.", file=sys.stderr)
assert False
return (
None,
{
curNode.getName():
AST.Pool(
poolType,
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
{
AST.PaddingKeysDict.FH: kSizeH,
AST.PaddingKeysDict.FW: kSizeW,
AST.PaddingKeysDict.zPadHLeft: zPadHLeft,
AST.PaddingKeysDict.zPadHRight: zPadHRight,
AST.PaddingKeysDict.zPadWLeft: zPadWLeft,
AST.PaddingKeysDict.zPadWRight: zPadWRight,
AST.PaddingKeysDict.strideH: strideH,
AST.PaddingKeysDict.strideW: strideW,
},
)
},
)
def VariableV2(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
curNodeShapeLi = curNode.getAttrMapRef()["shape"].getShape().getDimRef(
)[:]
curNodeInputType = curNode.getAttrMapRef()["dtype"].getDataType()
# NOTE : since this becomes an input node right now, i have also added to be prefixed at top in ProcessTFGraph::prefixAllPlaceHolderNodes()
# NOTE: There has to be some way for Athos to differentiate model from image, since in the compiled code
# (in the scenario of secure inference), model is input by server and image by client.
# We assume in the following that the PlaceHolder op node represents the image and
# all model parameters are represented using Variable op nodes.
# Hence, in the call to AST.Input, we pass inputByParty as SERVER.
return (
None,
{
curNode.getName():
AST.Input(
curNodeShapeLi,
curNodeInputType.name,
isSecret=True,
inputByParty=AST.Party.SERVER,
)
},
)
def Pad(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
# Mode refers to 'CONSTANT', 'REFLECT' or 'SYMMETRIC'
mode = 0
if ("\"mode\"" in curNode.getAttrMapRef()):
mode = curNode.getAttrMapRef()["\"mode\""].getI()
constant_values = 0
if ("\"constant_values\"" in curNode.getAttrMapRef()):
constant_values = curNode.getAttrMapRef(
)["\"constant_values\""].getI()
assert (
mode == 0 and constant_values == 0
) # For now to make life easy - deal with SYMMETRIC AND REFLECT when time comes
inputsRef = curNode.getInputsRef()
inputTensorShapeLi = extraNodeInfoDict[inputsRef[0]][0]
return (None,
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Pad.name, [
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]])
],
outputDiffInpDims=1))
def Conv3DBackpropInputV2(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
assert(len(inputsRef)==3) #output_shape, filter, input
stridesUsed = curNode.getAttrMapRef()["strides"].getList().getILi()
assert(stridesUsed[0]==1 and stridesUsed[4]==1)
strideD = stridesUsed[1]
strideH = stridesUsed[2]
strideW = stridesUsed[3]
filterShape = extraNodeInfoDict[inputsRef[1]][0]
FD = filterShape[0]
FH = filterShape[1]
FW = filterShape[2]
inputShape = extraNodeInfoDict[inputsRef[2]][0]
inputD = inputShape[1]
inputH = inputShape[2]
inputW = inputShape[3]
outputShape = extraNodeInfoDict[curNode.getName()][0]
outputD = outputShape[1]
outputH = outputShape[2]
outputW = outputShape[3]
paddingUsedStr = curNode.getAttrMapRef()["padding"].getS()
# Important: Using outputH and outputW in the below is not an error!
# For convTranspose, the parameters passed in the node are of the conv of which this convTranspose is an inverse.
# Which is why the call to helper_findPadding makes sense.
# The zPads below are of the conv of which this convTranspose is an inverse.
[zPadDLeft, zPadDRight, zPadHLeft, zPadHRight, zPadWLeft, zPadWRight] = TFNodesAST.helper_findPadding(outputH, outputW, FH, FW, strideH, strideW, paddingUsedStr, imgD = outputD, FD = FD, strideD = strideD)
options = {}
options[AST.PaddingKeysDict.FD] = FD
options[AST.PaddingKeysDict.FH] = FH
options[AST.PaddingKeysDict.FW] = FW
options[AST.PaddingKeysDict.zPadDLeft] = zPadDLeft
options[AST.PaddingKeysDict.zPadDRight] = zPadDRight
options[AST.PaddingKeysDict.zPadHLeft] = zPadHLeft
options[AST.PaddingKeysDict.zPadHRight] = zPadHRight
options[AST.PaddingKeysDict.zPadWLeft] = zPadWLeft
options[AST.PaddingKeysDict.zPadWRight] = zPadWRight
options[AST.PaddingKeysDict.strideD] = strideD
options[AST.PaddingKeysDict.strideH] = strideH
options[AST.PaddingKeysDict.strideW] = strideW
options[AST.PaddingKeysDict.ConvDim] = 3
options[AST.PaddingKeysDict.outputImgD] = outputD
options[AST.PaddingKeysDict.outputImgH] = outputH
options[AST.PaddingKeysDict.outputImgW] = outputW
return (None, { curNode.getName() : AST.BOp(AST.ID(dictNodeNameToOutVarStr[inputsRef[2]]),
TFNodesAST.getOperatorsIdx('#T'),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]),
options)})
def VariableV2(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
curNodeShapeLi = curNode.getAttrMapRef()["\"shape\""].getShape(
).getDimRef()[:]
curNodeInputType = curNode.getAttrMapRef()["\"dtype\""].getDataType()
#########TODO_TAB : for inference, have commented out decl and inserted input nodes.
# TODO : Right now in the current implementation, the dataType being passed to the node is being ignored by SeeDot.
# Fix this later.
# return (None, AST.Decl(curNodeShapeLi, curNodeInputType.name, None))
# NOTE : since this becomes an input node right now, i have also added to be prefixed at top in ProcessTFGraph::prefixAllPlaceHolderNodes()
return (None, AST.Input(curNodeShapeLi, curNodeInputType.name))
def Conv2D(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
inputsRef = curNode.getInputsRef()
assert len(inputsRef) == 2
stridesUsed = curNode.getAttrMapRef()["strides"].getList().getILi()
assert stridesUsed[0] == 1 and stridesUsed[3] == 1
strideH = stridesUsed[1]
strideW = stridesUsed[2]
inputShape = extraNodeInfoDict[inputsRef[0]][0]
imgH = inputShape[1]
imgW = inputShape[2]
filterShape = extraNodeInfoDict[inputsRef[1]][0]
FH = filterShape[0]
FW = filterShape[1]
paddingUsedStr = curNode.getAttrMapRef()["padding"].getS()
[zPadHLeft, zPadHRight, zPadWLeft,
zPadWRight] = TFNodesAST.helper_findPadding(imgH, imgW, FH, FW,
strideH, strideW,
paddingUsedStr)
options = {}
options[AST.PaddingKeysDict.FH] = FH
options[AST.PaddingKeysDict.FW] = FW
options[AST.PaddingKeysDict.zPadHLeft] = zPadHLeft
options[AST.PaddingKeysDict.zPadHRight] = zPadHRight
options[AST.PaddingKeysDict.zPadWLeft] = zPadWLeft
options[AST.PaddingKeysDict.zPadWRight] = zPadWRight
options[AST.PaddingKeysDict.strideH] = strideH
options[AST.PaddingKeysDict.strideW] = strideW
return (
None,
{
curNode.getName():
AST.BOp(
AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
TFNodesAST.getOperatorsIdx("#"),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]),
options,
)
},
)
def Conv3D(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
assert (len(inputsRef) == 2)
stridesUsed = curNode.getAttrMapRef()["strides"].getList().getILi()
assert (stridesUsed[0] == 1 and stridesUsed[4] == 1)
strideD = stridesUsed[1]
strideH = stridesUsed[2]
strideW = stridesUsed[3]
inputShape = extraNodeInfoDict[inputsRef[0]][0]
imgD = inputShape[1]
imgH = inputShape[2]
imgW = inputShape[3]
filterShape = extraNodeInfoDict[inputsRef[1]][0]
FD = filterShape[0]
FH = filterShape[1]
FW = filterShape[2]
paddingUsedStr = curNode.getAttrMapRef()["padding"].getS()
[zPadDLeft, zPadDRight, zPadHLeft, zPadHRight, zPadWLeft,
zPadWRight] = TFNodesAST.helper_findPadding(imgH, imgW, FH, FW,
strideH, strideW,
paddingUsedStr, imgD, FD,
strideD)
options = {}
options[AST.PaddingKeysDict.FD] = FD
options[AST.PaddingKeysDict.FH] = FH
options[AST.PaddingKeysDict.FW] = FW
options[AST.PaddingKeysDict.zPadDLeft] = zPadDLeft
options[AST.PaddingKeysDict.zPadDRight] = zPadDRight
options[AST.PaddingKeysDict.zPadHLeft] = zPadHLeft
options[AST.PaddingKeysDict.zPadHRight] = zPadHRight
options[AST.PaddingKeysDict.zPadWLeft] = zPadWLeft
options[AST.PaddingKeysDict.zPadWRight] = zPadWRight
options[AST.PaddingKeysDict.strideD] = strideD
options[AST.PaddingKeysDict.strideH] = strideH
options[AST.PaddingKeysDict.strideW] = strideW
options[AST.PaddingKeysDict.ConvDim] = 3
return (None, {
curNode.getName():
AST.BOp(AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
TFNodesAST.getOperatorsIdx('#'),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]), options)
})
def Mean(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
inputsRef = curNode.getInputsRef()
attrMapRef = curNode.getAttrMapRef()
assert (len(inputsRef) == 2)
keepdims = False
if ("keep_dims" in attrMapRef):
keepdims = attrMapRef["keep_dims"].getB()
reductionAxesNodeName = inputsRef[1]
redAxesN = graph.__getitem__(reductionAxesNodeName)
redAxesT = redAxesN.getAttrVal("value").getTensor()
rank = redAxesT.getShapeRef().getRank()
if rank != 0:
reductionAxesList = redAxesT.getContentAsValArr()
else:
reductionAxesList = [redAxesT.getConstantVal()]
curNodeShapeLi = extraNodeInfoDict[curNode.getName()][0]
return (None, {
curNode.getName():
AST.Reduce(AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
keepdims, curNodeShapeLi,
TFNodesAST.getOperatorsIdx('mean'), reductionAxesList)
})
def Placeholder(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
curNodeShapeLi = extraNodeInfoDict[curNode.getName()][0]
curNodeInputType = curNode.getAttrMapRef()["dtype"].getDataType()
assert curNodeInputType is not Graph.DataTypeEnum.DT_INVALID
# NOTE: There has to be some way for Athos to differentiate model from image, since in the compiled code
# (in the scenario of secure inference), model is input by server and image by client.
# We assume in the following that the PlaceHolder op node represents the image and
# all model parameters are represented using Variable op nodes.
# Hence, in the call to AST.Input, we pass inputByParty=1.
return (
None,
{
curNode.getName():
AST.Input(
curNodeShapeLi,
curNodeInputType.name,
isSecret=True,
inputByParty=AST.Party.CLIENT,
)
},
)
def MatMul(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
inputsRef = curNode.getInputsRef()
assert len(inputsRef) == 2
inp1Str = dictNodeNameToOutVarStr[inputsRef[0]]
inp2Str = dictNodeNameToOutVarStr[inputsRef[1]]
inp1AST = AST.ID(inp1Str)
inp2AST = AST.ID(inp2Str)
attrMapRef = curNode.getAttrMapRef()
transposeABool = transposeBBool = False
if "transpose_a" in attrMapRef:
transposeABool = attrMapRef["transpose_a"].getB()
if "transpose_b" in attrMapRef:
transposeBBool = attrMapRef["transpose_b"].getB()
if transposeABool:
inp1AST = AST.Transp(inp1AST)
if transposeBBool:
inp2AST = AST.Transp(inp2AST)
return (
None,
{
curNode.getName():
AST.BOp(inp1AST, TFNodesAST.getOperatorsIdx("*"), inp2AST)
},
)
def Squeeze(
graph: Graph.Graph,
curNode: Graph.Node,
dictNodeNameToOutVarStr: dict,
extraNodeInfoDict: dict,
):
inputsRef = curNode.getInputsRef()
inputTensorShape = extraNodeInfoDict[inputsRef[0]][0]
inputTensorRank = len(inputTensorShape)
squeezeDims = curNode.getAttrMapRef()["squeeze_dims"].getList().getILi(
)
squeezeDimsRank = len(squeezeDims)
return (
None,
{
curNode.getName():
AST.UninterpFuncCall(
extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Squeeze.name,
list(
map(lambda x: AST.Int(x, 32, isSecret=False),
squeezeDims)) +
[AST.ID(dictNodeNameToOutVarStr[inputsRef[0]])],
)
},
)
def Placeholder(graph: Graph.Graph, curNode: Graph.Node,
dictNodeNameToOutVarStr: dict, extraNodeInfoDict: dict):
#curNodeShapeLi = curNode.getAttrMapRef()["\"shape\""].getShape().getDimRef()
curNodeShapeLi = extraNodeInfoDict[curNode.getName()][0]
curNodeInputType = curNode.getAttrMapRef()["\"dtype\""].getDataType()
assert (curNodeInputType is not Graph.DataTypeEnum.DT_INVALID)
#TODO : There has to be some way to take range, understand the dimensions for SeeDot
return (None, AST.Input(curNodeShapeLi, curNodeInputType.name))
def ZerosLike(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
assert(len(inputsRef)==1)
curNodeOutputType = curNode.getAttrMapRef()["T"].getDataType()
assert(curNodeOutputType is not Graph.DataTypeEnum.DT_INVALID)
retAST = AST.UninterpFuncCall(extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.CreateTensor.name,
[AST.Int(0, isSecret=False)],
isSecret=False)
return (None, { curNode.getName() : retAST})
def Slice(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict): inputsRef = curNode.getInputsRef() assert(len(inputsRef) == 3) curNodeDataType = curNode.getAttrMapRef()["\"T\""].getDataType() retAST = AST.UninterpFuncCall(extraNodeInfoDict[curNode.getName()][0], TFNodesAST.UninterpFuncCallNames.CreateCopy.name, [AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]), # of this AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]), # begin idx AST.ID(dictNodeNameToOutVarStr[inputsRef[2]]) # size ]) return (None, retAST)
def Mean(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
attrMapRef = curNode.getAttrMapRef()
assert(len(inputsRef) == 2)
keepdims = False
if ("\"keep_dims\"" in attrMapRef):
keepdims = attrMapRef["\"keep_dims\""].getB()
curNodeShapeLi = extraNodeInfoDict[curNode.getName()][0]
return (None, AST.Reduce(AST.ID(dictNodeNameToOutVarStr[inputsRef[0]]),
AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]),
AST.Int(int(keepdims), 32, isSecret=False),
curNodeShapeLi,
TFNodesAST.getOperatorsIdx('mean')))
def ConcatV2(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
N = curNode.getAttrMapRef()["N"].getI()
assert(len(inputsRef) == N+1) #One extra for axis
#TODO : Since the axis of concat is constant, therefore, its known here - the input's sizes along that dim should be
# passed as input to the below function.
# For now hardcoding.
retAST = AST.UninterpFuncCall(extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.Concat.name + str(N) + 'T',
list(map(lambda x : AST.ID(dictNodeNameToOutVarStr[x]), inputsRef)),
outputDiffInpDims=1
)
return (None, { curNode.getName() : retAST})
def Fill(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
assert(len(inputsRef) == 2)
curNodeOutputShape = extraNodeInfoDict[inputsRef[0]][0]
assert(len(curNodeOutputShape) == 1) #inputsRef[0] denotes a shape and should have a rank of 1
curNodeOutputType = curNode.getAttrMapRef()["T"].getDataType()
assert(curNodeOutputType is not Graph.DataTypeEnum.DT_INVALID)
retAST = AST.UninterpFuncCall(extraNodeInfoDict[curNode.getName()][0],
TFNodesAST.UninterpFuncCallNames.CreateTensor.name,
[AST.ID(dictNodeNameToOutVarStr[inputsRef[1]]) ],
isSecret=False)
return (None, { curNode.getName() : retAST})
def Identity(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
#In SeeDot, J2=J1 creates a new reference for J1 -- so
# the corresponding code in Seedot cannot simply be J2 = J1.
# Instead create a new tensor first and then assign the old one to the new one.
inputsRef = curNode.getInputsRef()
assert(len(inputsRef)==1)
curNodeDataType = curNode.getAttrMapRef()["T"].getDataType()
assert(curNodeDataType is not Graph.DataTypeEnum.DT_INVALID)
curNodeShape = extraNodeInfoDict[curNode.getName()][0]
retAST = AST.UninterpFuncCall(curNodeShape,
TFNodesAST.UninterpFuncCallNames.CreateIdentity.name,
[AST.ID(dictNodeNameToOutVarStr[inputsRef[0]])])
return (None, { curNode.getName() : retAST})
def MatMul(graph : Graph.Graph, curNode : Graph.Node, dictNodeNameToOutVarStr : dict, extraNodeInfoDict : dict):
inputsRef = curNode.getInputsRef()
assert(len(inputsRef) == 2)
inp1Str = dictNodeNameToOutVarStr[inputsRef[0]]
inp2Str = dictNodeNameToOutVarStr[inputsRef[1]]
inp1AST = AST.ID(inp1Str)
inp2AST = AST.ID(inp2Str)
attrMapRef = curNode.getAttrMapRef()
transposeABool = transposeBBool = False
if ("\"transpose_a\"" in attrMapRef):
transposeABool = attrMapRef["\"transpose_a\""].getB()
if ("\"transpose_b\"" in attrMapRef):
transposeBBool = attrMapRef["\"transpose_b\""].getB()
if (transposeABool): inp1AST = AST.Transp(inp1AST)
if (transposeBBool): inp2AST = AST.Transp(inp2AST)
return (None, AST.BOp(inp1AST, TFNodesAST.getOperatorsIdx('*'), inp2AST))
