#
# For documentation and usage, see https://www.unicode.org/reports/tr46
#
0000..002C    ; disallowed_STD3_valid                  # 1.1  <control-0000>..COMMA
002D..002E    ; valid                                  # 1.1  HYPHEN-MINUS..FULL STOP
002F          ; disallowed_STD3_valid                  # 1.1  SOLIDUS
0030..0039    ; valid                                  # 1.1  DIGIT ZERO..DIGIT NINE

Edge, as it looks bad and cannot be made to\n    // match the appearance of the native widget.\n    // See https://github.com/twbs/bootstrap/issues/19398.\n    color: $input-color;\n    background-color: $input-bg;\n  }\n}\n\n// Make file inputs better match text inputs by forcing them to new lines.\n.form-control-file,\n.form-control-range {\n  display: block;\n  width: 100%;\n}\n\n\n//\n// Labels\n//\n\n// For use with horizontal and inline forms, when you need the label (or legend)\n// text to align...

Edge, as it looks bad and cannot be made to\n    // match the appearance of the native widget.\n    // See https://github.com/twbs/bootstrap/issues/19398.\n    color: $input-color;\n    background-color: $input-bg;\n  }\n}\n\n// Make file inputs better match text inputs by forcing them to new lines.\n.form-control-file,\n.form-control-range {\n  display: block;\n  width: 100%;\n}\n\n\n//\n// Labels\n//\n\n// For use with horizontal and inline forms, when you need the label (or legend)\n// text to align...

- Kubeadm increased to 5 minutes its timeout for the TLS bootstrapping process to complete upon join ([#89735](https://github.com/kubernetes/kubernetes/pull/89735), [@rosti](https://github.com/rosti)) [SIG Cluster Lifecycle]

        StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs):
        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],