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],

@media print {\n    *,\n    *::before,\n    *::after {\n      // Bootstrap specific; comment out `color` and `background`\n      //color: $black !important; // Black prints faster\n      text-shadow: none !important;\n      //background: transparent !important;\n      box-shadow: none !important;\n    }\n\n    a {\n      &:not(.btn) {\n        text-decoration: underline;\n      }\n    }\n\n    // Bootstrap specific; comment the following selector out\n    //a[href]::after {\n    //  content: \" (\" attr(href)...

@media print {\n    *,\n    *::before,\n    *::after {\n      // Bootstrap specific; comment out `color` and `background`\n      //color: $black !important; // Black prints faster\n      text-shadow: none !important;\n      //background: transparent !important;\n      box-shadow: none !important;\n    }\n\n    a {\n      &:not(.btn) {\n        text-decoration: underline;\n      }\n    }\n\n    // Bootstrap specific; comment the following selector out\n    //a[href]::after {\n    //  content: \" (\" attr(href)...

    analyze programmatically. By using aliases here, we would need to handle
    potentially aliased go_binary targets and dereference to the effective
    target.
  
    The comment references an issue with `pure = select(...)` which appears
    to be resolved considering this now builds.
  
  - make kube::util::find-binary not dependent on bazel-out/ structure