MMMM YYYY HH:mm"},calendar:{sameDay:"[Inniu ag] LT",nextDay:"[Am\xe1rach ag] LT",nextWeek:"dddd [ag] LT",lastDay:"[Inn\xe9 ag] LT",lastWeek:"dddd [seo caite] [ag] LT",sameElse:"L"},relativeTime:{future:"i %s",past:"%s \xf3 shin",s:"c\xfapla soicind",ss:"%d soicind",m:"n\xf3im\xe9ad",mm:"%d n\xf3im\xe9ad",h:"uair an chloig",hh:"%d uair an chloig",d:"l\xe1",dd:"%d l\xe1",M:"m\xed",MM:"%d m\xedonna",y:"bliain",yy:"%d bliain"},dayOfMonthOrdinalParse:/\d{1,2}(d|na|mh)/,ordinal:function(e){return e+(1===...

}

// TrimLeftFunc treats s as UTF-8-encoded bytes and returns a subslice of s by slicing off
// all leading UTF-8-encoded code points c that satisfy f(c).
func TrimLeftFunc(s []byte, f func(r rune) bool) []byte {
	i := indexFunc(s, f, false)
	if i == -1 {
		return nil
	}
	return s[i:]
}

// TrimRightFunc returns a subslice of s by slicing off all trailing
// UTF-8-encoded code points c that satisfy f(c).

The <i>capacity</i> is a measure of that extent: it is the sum of
the length of the slice and the length of the array beyond the slice;
a slice of length up to that capacity can be created by
<a href="#Slice_expressions"><i>slicing</i></a> a new one from the original slice.
The capacity of a slice <code>a</code> can be discovered using the
built-in function <a href="#Length_and_capacity"><code>cap(a)</code></a>.
</p>

<p>

The <i>capacity</i> is a measure of that extent: it is the sum of
the length of the slice and the length of the array beyond the slice;
a slice of length up to that capacity can be created by
<a href="#Slice_expressions"><i>slicing</i></a> a new one from the original slice.
The capacity of a slice <code>a</code> can be discovered using the
built-in function <a href="#Length_and_capacity"><code>cap(a)</code></a>.
</p>

<p>

        *   `tf.data.experimental.OptimizationOptions.map_vectorization` *
            `tf.data.experimental.OptimizationOptions.reorder_data_discarding_ops`
*   `tf.keras`:
    *   Fix usage of `__getitem__` slicing in Keras Functional APIs when the
        inputs are `RaggedTensor` objects.
    *   Add `keepdims` argument to all `GlobalPooling` layers.
    *   Add `include_preprocessing` argument to `MobileNetV3` architectures to