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,
        kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2],

pkg testing, method (*T) Cleanup(func())
pkg testing, type TB interface, Cleanup(func())
pkg unicode, const Version = "12.0.0"
pkg unicode, var Elymaic *RangeTable
pkg unicode, var Nandinagari *RangeTable
pkg unicode, var Nyiakeng_Puachue_Hmong *RangeTable
pkg unicode, var Wancho *RangeTable
# freebsd arm64 port
pkg log/syslog (freebsd-arm64), const LOG_ALERT = 1
pkg log/syslog (freebsd-arm64), const LOG_ALERT Priority

	VDPPS $7, Y2, Y15, Y11                  // c4630540da07
	VDPPS $7, Y11, Y15, Y11                 // c4430540db07
	VERR (BX)                               // 0f0023
	VERR (R11)                              // 410f0023
	VERR DX                                 // 0f00e2
	VERR R11                                // 410f00e3
	VERW (BX)                               // 0f002b