ADDL $7, (R11)                          // 41830307
	ADDL $7, DX                             // 83c207
	ADDL $7, R11                            // 4183c307
	ADDL DX, (BX)                           // 0113
	ADDL R11, (BX)                          // 44011b
	ADDL DX, (R11)                          // 410113
	ADDL R11, (R11)                         // 45011b
	ADDL DX, DX                             // 01d2 or 03d2

06:33:33,2014-03-07 06:58:54,N,1,-73.893325805664063,40.757320404052734,-73.913200378417969,40.67413330078125,1,12.40,36,0,0.5,1,0,,,37.5,1,,129,177,green,0.00,3.9,0.0,37,26,7.83,1298,309.02,4,Queens,030902,4030902,E,QN28,Jackson Heights,4102,2142,365.01,3,Brooklyn,036501,3036501,E,BK79,Ocean Hill,4007^3389488,1,2014-03-03 09:57:36,2014-03-03 10:08:28,N,1,-73.897117614746094,40.754409790039063,-73.927032470703125,40.747509002685547,1,1.90,10,0,0.5,2.1,0,,,12.6,1,,129,226,green,0.04,5.1,0.1,32,17,6.7...

* Updates to use debian-iptables v11.0, debian-hyperkube-base 0.12.0, and kube-addon-manager:v8.9. ([#70209](https://github.com/kubernetes/kubernetes/pull/70209), [@ixdy](https://github.com/ixdy))

        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],
        keepdims=True) # Author code uses std + 1e-5 return