2169          ; mapped                 ; 0078          # 1.1  ROMAN NUMERAL TEN
216A          ; mapped                 ; 0078 0069     # 1.1  ROMAN NUMERAL ELEVEN
216B          ; mapped                 ; 0078 0069 0069 #1.1  ROMAN NUMERAL TWELVE
216C          ; mapped                 ; 006C          # 1.1  ROMAN NUMERAL FIFTY
216D          ; mapped                 ; 0063          # 1.1  ROMAN NUMERAL ONE HUNDRED

    inputs. Please see below for additional details on these layers.

*   TFLite now properly supports dynamic shapes during conversion and inference.
    We’ve also added opt-in support on Android and iOS for
    [XNNPACK](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/delegates/xnnpack),
    a highly optimized set of CPU kernels, as well as opt-in support for

deprecated.\n//\n// Originally added during our alphas and maintained during betas, this mixin was\n// designed to prevent `:hover` stickiness on iOS-an issue where hover styles\n// would persist after initial touch.\n//\n// For backward compatibility, we've kept these mixins and updated them to\n// always return their regular pseudo-classes instead of a shimmed media query.\n//\n// Issue: https://github.com/twbs/bootstrap/issues/25195\n\n@mixin hover() {\n  &:hover { @content; }\n}\n\n@mixin hover-focus()...

*Digest) readGeneric(out []byte) (n int, err error) { // If we're still absorbing, pad and apply the permutation. if d.state == spongeAbsorbing { d.padAndPermute() } n = len(out) // Now, do the squeezing. for len(out) > 0 { // Apply the permutation if we've squeezed the sponge dry. if d.n == d.rate { d.permute() } x := copy(out, d.a[d.n:d.rate]) d.n += x out = out[x:] } return } // Sum appends the current hash to b and returns the resulting slice. // It does not change the underlying hash state. func...