Type","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallback","open","username","xhrFields","onload","onerror","onabort","ontimeout","onreadystatechange","responseType","responseText","binary","scriptAttrs","charset","scriptCharset","evt","oldCallbacks","rjsonp","json...

	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

* 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