or assignment are not yet evaluated.
</li>

<li>
If one or more of the communications can proceed,
a single one that can proceed is chosen via a uniform pseudo-random selection.
Otherwise, if there is a default case, that case is chosen.
If there is no default case, the "select" statement blocks until
at least one of the communications can proceed.
</li>

<li>

ng\xe0y",dd:"%d ng\xe0y",w:"m\u1ed9t tu\u1ea7n",ww:"%d tu\u1ea7n",M:"m\u1ed9t th\xe1ng",MM:"%d th\xe1ng",y:"m\u1ed9t n\u0103m",yy:"%d n\u0103m"},dayOfMonthOrdinalParse:/\d{1,2}/,ordinal:function(e){return e},week:{dow:1,doy:4}}),M.defineLocale("x-pseudo",{months:"J~\xe1\xf1\xfa\xe1~r\xfd_F~\xe9br\xfa~\xe1r\xfd_~M\xe1rc~h_\xc1p~r\xedl_~M\xe1\xfd_~J\xfa\xf1\xe9~_J\xfal~\xfd_\xc1\xfa~g\xfast~_S\xe9p~t\xe9mb~\xe9r_\xd3~ct\xf3b~\xe9r_\xd1~\xf3v\xe9m~b\xe9r_~D\xe9c\xe9~mb\xe9r".split("_"),monthsShort:...

the same across browsers. Vendor-specific rules for pseudo\n// elements cannot be mixed. As such, there are no shared styles for focus or\n// active states on prefixed selectors.\n\n.custom-range {\n  width: 100%;\n  height: add($custom-range-thumb-height, $custom-range-thumb-focus-box-shadow-width * 2);\n  padding: 0; // Need to reset padding\n  background-color: transparent;\n  appearance: none;\n\n  &:focus {\n    outline: 0;\n\n    // Pseudo-elements must be split across multiple rulesets to have...

or assignment are not yet evaluated.
</li>

<li>
If one or more of the communications can proceed,
a single one that can proceed is chosen via a uniform pseudo-random selection.
Otherwise, if there is a default case, that case is chosen.
If there is no default case, the "select" statement blocks until
at least one of the communications can proceed.
</li>

<li>

*   Clean up `BatchNormalization` layer's `trainable` property to act like
    standard python state when it's used inside `tf.functions` (frozen at
    tracing time), instead of acting like a pseudo-variable whose updates *kind
    of sometimes* get reflected in already-traced `tf.function` traces.
*   Add the `Conv1DTranspose` layer.
*   Refine the semantics of `SensitivitySpecificityBase` derived metrics. See