10 void ApplyLayerNorm(const int16_t* input, const int16_t* layer_norm_weights, const int32_t* bias,  in ApplyLayerNorm()
12                     int n_batch, int n_input, int16_t* output) {  in ApplyLayerNorm()
48             output[index] = static_cast<int16_t>(val5);  in ApplyLayerNorm()
86 void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input, int16_t* output) {  in ApplySigmoid()
89             using F3 = gemmlowp::FixedPoint<std::int16_t, 3>;  in ApplySigmoid()
90             using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;  in ApplySigmoid()
99 void CwiseMul(const int16_t* input_1, const int16_t* input_2, int n_batch, int n_input, int shift,  in CwiseMul()
100               int16_t* output) {  in CwiseMul()
104             const int16_t a = input_1[index];  in CwiseMul()
105             const int16_t b = input_2[index];  in CwiseMul()
107             output[index] = static_cast<int16_t>(gemmlowp::RoundingDivideByPOT(value, shift));  in CwiseMul()
112 void CwiseMul(const int16_t* input_1, const int16_t* input_2, int32_t multiplier, int32_t shift,  in CwiseMul()
117             const int16_t a = input_1[index];  in CwiseMul()
118             const int16_t b = input_2[index];  in CwiseMul()
138 void CwiseAdd(const int16_t* input_1, const int16_t* input_2, int n_batch, int n_input,  in CwiseAdd()
139               int16_t* output) {  in CwiseAdd()
145             output[index] = static_cast<int16_t>(sum_clamped);  in CwiseAdd()
150 void CwiseClipping(int16_t* input, const int16_t clipping_value, int32_t n_batch, int32_t n_input) {  in CwiseClipping()
178 void VectorBatchVectorCwiseProductAccumulate(const int16_t* vector, int v_size,  in VectorBatchVectorCwiseProductAccumulate()
179                                              const int16_t* batch_vector, int n_batch,  in VectorBatchVectorCwiseProductAccumulate()
180                                              int32_t multiplier, int shift, int16_t* result) {  in VectorBatchVectorCwiseProductAccumulate()