g-x;\n  color: $dropdown-link-color;\n}\n","@mixin caret-down() {\n  border-top: $caret-width solid;\n  border-right: $caret-width solid transparent;\n  border-bottom: 0;\n  border-left: $caret-width solid transparent;\n}\n\n@mixin caret-up() {\n  border-top: 0;\n  border-right: $caret-width solid transparent;\n  border-bottom: $caret-width solid;\n  border-left: $caret-width solid transparent;\n}\n\n@mixin caret-right() {\n  border-top: $caret-width solid transparent;\n  border-right: 0;\n  border-bottom:...

border-right: 0;\n  border-bottom: $width solid transparent;\n  border-left: $width solid;\n}\n\n@mixin caret-start($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: $width solid;\n  border-bottom: $width solid transparent;\n}\n\n@mixin caret(\n  $direction: down,\n  $width: $caret-width,\n  $spacing: $caret-spacing,\n  $vertical-align: $caret-vertical-align\n) {\n  @if $enable-caret {\n    &::after {\n      display: inline-block;\n      margin-left: $spacing;\n      vertical-align:...

0F35          ; valid                                  # 2.0  TIBETAN MARK NGAS BZUNG NYI ZLA
0F36          ; valid                  ;      ; NV8    # 2.0  TIBETAN MARK CARET -DZUD RTAGS BZHI MIG CAN
0F37          ; valid                                  # 2.0  TIBETAN MARK NGAS BZUNG SGOR RTAGS
0F38          ; valid                  ;      ; NV8    # 2.0  TIBETAN MARK CHE MGO

        TPUs. See
        [usage guide](https://www.tensorflow.org/guide/keras/mixed_precision).
    *   Introduced the `TextVectorization` layer, which takes as input raw
        strings and takes care of text standardization, tokenization, n-gram
        generation, and vocabulary indexing. See this
        [end-to-end text classification example](https://colab.research.google.com/drive/1RvCnR7h0_l4Ekn5vINWToI9TNJdpUZB3).

The paper suggests in Section 4 that we can do an "Almost Montgomery // Multiplication" by subtracting only in the overflow case, but the // cost is very similar since the constant time subtraction tells us if // x >= m as a side effect, and taking care of the broken invariant is // highly undesirable (see https://go.dev/issue/13907). copy(x.reset(n).limbs, T[n:]) x.maybeSubtractModulus(choice(c), m) // The following specialized cases follow the exact same algorithm, but // optimized for the sizes...