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],

background-color: $body-bg; // 2\n}\n\n// Future-proof rule: in browsers that support :focus-visible, suppress the focus outline\n// on elements that programmatically receive focus but wouldn't normally show a visible\n// focus outline. In general, this would mean that the outline is only applied if the\n// interaction that led to the element receiving programmatic focus was a keyboard interaction,\n// or the browser has somehow determined that the user is primarily a keyboard user and/or\n// wants focus outlines...

background-color: $body-bg; // 2\n}\n\n// Future-proof rule: in browsers that support :focus-visible, suppress the focus outline\n// on elements that programmatically receive focus but wouldn't normally show a visible\n// focus outline. In general, this would mean that the outline is only applied if the\n// interaction that led to the element receiving programmatic focus was a keyboard interaction,\n// or the browser has somehow determined that the user is primarily a keyboard user and/or\n// wants focus outlines...

2F95          ; mapped                 ; 8C37          # 3.0  KANGXI RADICAL VALLEY
2F96          ; mapped                 ; 8C46          # 3.0  KANGXI RADICAL BEAN
2F97          ; mapped                 ; 8C55          # 3.0  KANGXI RADICAL PIG
2F98          ; mapped                 ; 8C78          # 3.0  KANGXI RADICAL BADGER