137     uint32_t outHeight = getSizeOfDimension(outputShape, 1);                      \
138     uint32_t outWidth = getSizeOfDimension(outputShape, 2);                       \
145     im2colDim.sizes[3] = (int)getSizeOfDimension(outputShape, 0);                 \
146     im2colDim.sizes[2] = (int)getSizeOfDimension(outputShape, 1);                 \
147     im2colDim.sizes[1] = (int)getSizeOfDimension(outputShape, 2);                 \
198               float* outputData, const Shape& outputShape) {  in convNhwc()  argument
218             convertShapeToDims(outputShape), need_im2colData ? im2colData : nullptr, im2colDim);  in convNhwc()
227               uint8_t* outputData, const Shape& outputShape) {  in convNhwc()  argument
234     int32_t outputOffset = outputShape.offset;  in convNhwc()
242     NN_RET_CHECK(GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape, outputShape,  in convNhwc()
247     CalculateActivationRangeUint8(activation, outputShape, &output_activation_min,  in convNhwc()
269                                 convertShapeToDims(outputShape),  in convNhwc()
281               int8_t* outputData, Shape outputShape) {  in convNhwc()  argument
292     std::vector<uint8_t> unsignedOutput(getNumberOfElements(outputShape));  in convNhwc()
293     outputShape.offset += 128;  in convNhwc()
298                           dilation_height_factor, activation, unsignedOutput.data(), outputShape));  in convNhwc()
310               _Float16* outputData, const Shape& outputShape) {  in convNhwc()  argument
316     std::vector<float> outputData_float32(getNumberOfElements(outputShape));  in convNhwc()
325              dilation_height_factor, activation, outputData_float32.data(), outputShape);  in convNhwc()
337           const Shape& outputShape) {  in conv()  argument
341     NN_RET_CHECK(output.initialize(outputData, outputShape));  in conv()
358                               const Shape& outputShape) {  in convQuant8PerChannelNhwc()  argument
368     uint32_t outputHeight = getSizeOfDimension(outputShape, 1);  in convQuant8PerChannelNhwc()
369     uint32_t outputWidth = getSizeOfDimension(outputShape, 2);  in convQuant8PerChannelNhwc()
370     uint32_t outputDepth = getSizeOfDimension(outputShape, 3);  in convQuant8PerChannelNhwc()
373     int32_t outputOffset = outputShape.offset;  in convQuant8PerChannelNhwc()
385                 inputShape, filterChannelShape, biasChannelShape, outputShape, &realMultiplier[i]));  in convQuant8PerChannelNhwc()
392     CalculateActivationRangeUint8(activation, outputShape, &output_activation_min,  in convQuant8PerChannelNhwc()
451                               const Shape& outputShape) {  in convQuant8PerChannelNhwc()  argument
461     uint32_t outputHeight = getSizeOfDimension(outputShape, 1);  in convQuant8PerChannelNhwc()
462     uint32_t outputWidth = getSizeOfDimension(outputShape, 2);  in convQuant8PerChannelNhwc()
463     uint32_t outputDepth = getSizeOfDimension(outputShape, 3);  in convQuant8PerChannelNhwc()
466     int32_t outputOffset = outputShape.offset;  in convQuant8PerChannelNhwc()
478                 inputShape, filterChannelShape, biasChannelShape, outputShape, &realMultiplier[i]));  in convQuant8PerChannelNhwc()
483     CalculateActivationRangeInt8(activation, outputShape, &output_activation_min,  in convQuant8PerChannelNhwc()
488     convParams.output_offset = outputShape.offset;  in convQuant8PerChannelNhwc()
503             convertShapeToTflshape(outputShape), outputData);  in convQuant8PerChannelNhwc()
514                           T* outputData, const Shape& outputShape) {  in convQuant8PerChannel()  argument
518     NN_RET_CHECK(output.initialize(outputData, outputShape));  in convQuant8PerChannel()