LXVD2X (P1ptr+R19), Z2H LXVD2X (P1ptr+R20), Z2L VCMPEQUD SEL2, IDX, SEL1 // VCEQG SEL2, IDX, SEL1 OK // This will result in SEL1 being all 0s or 1s, meaning // the result is either X1L or X2L, no individual byte // selection. VSEL X1L, X2L, SEL1, X1L VSEL X1H, X2H, SEL1, X1H VSEL Y1L, Y2L, SEL1, Y1L VSEL Y1H, Y2H, SEL1, Y1H VSEL Z1L, Z2L, SEL1, Z1L VSEL Z1H, Z2H, SEL1, Z1H // Add 1 to all bytes in SEL2 VADDUBM SEL2, ONE, SEL2 // VAB SEL2, ONE, SEL2 OK ADD $96, P1ptr BDNZ loop_select // STXVD2X is used...

        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):
        mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True)
        return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var +
        1e-10))` Alternatively, you can override `convolution_op`: `python class
        StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs,