109 void compute_gru(const GRULayer *gru, float *state, const float *input)  in compute_gru()  argument
117    M = gru->nb_inputs;  in compute_gru()
118    N = gru->nb_neurons;  in compute_gru()
123       float sum = gru->bias[i];  in compute_gru()
125          sum += gru->input_weights[j*stride + i]*input[j];  in compute_gru()
127          sum += gru->recurrent_weights[j*stride + i]*state[j];  in compute_gru()
133       float sum = gru->bias[N + i];  in compute_gru()
135          sum += gru->input_weights[N + j*stride + i]*input[j];  in compute_gru()
137          sum += gru->recurrent_weights[N + j*stride + i]*state[j];  in compute_gru()
143       float sum = gru->bias[2*N + i];  in compute_gru()
145          sum += gru->input_weights[2*N + j*stride + i]*input[j];  in compute_gru()
147          sum += gru->recurrent_weights[2*N + j*stride + i]*state[j]*r[j];  in compute_gru()
148       if (gru->activation == ACTIVATION_SIGMOID) sum = sigmoid_approx(WEIGHTS_SCALE*sum);  in compute_gru()
149       else if (gru->activation == ACTIVATION_TANH) sum = tansig_approx(WEIGHTS_SCALE*sum);  in compute_gru()
150       else if (gru->activation == ACTIVATION_RELU) sum = relu(WEIGHTS_SCALE*sum);  in compute_gru()