*Nat) trim() *Nat { // Trim most significant (trailing in little-endian) zero limbs. // We assume comparison with zero (but not the branch) is constant time. for i := len(x.limbs) - 1; i >= 0; i-- { if x.limbs[i] != 0 { break } x.limbs = x.limbs[:i] } return x } // set assigns x = y, optionally resizing x to the appropriate size. func (x *Nat) set(y *Nat) *Nat { x.reset(len(y.limbs)) copy(x.limbs, y.limbs) return x } // Bits returns x as a little-endian slice of uint. The length of the slice // matches...

*Nat) trim() *Nat { // Trim most significant (trailing in little-endian) zero limbs. // We assume comparison with zero (but not the branch) is constant time. for i := len(x.limbs) - 1; i >= 0; i-- { if x.limbs[i] != 0 { break } x.limbs = x.limbs[:i] } return x } // set assigns x = y, optionally resizing x to the appropriate size. func (x *Nat) set(y *Nat) *Nat { x.reset(len(y.limbs)) copy(x.limbs, y.limbs) return x } // Bits returns x as a little-endian slice of uint. The length of the slice // matches...

0466          ; mapped                 ; 0467          # 1.1  CYRILLIC CAPITAL LETTER LITTLE YUS
0467          ; valid                                  # 1.1  CYRILLIC SMALL LETTER LITTLE YUS
0468          ; mapped                 ; 0469          # 1.1  CYRILLIC CAPITAL LETTER IOTIFIED LITTLE YUS
0469          ; valid                                  # 1.1  CYRILLIC SMALL LETTER IOTIFIED LITTLE YUS

takes arguments in reversed order and with different keywords. In particular we
now match NumPy order as `tf.concat(values, axis, name)`. *
`tf.image.decode_jpeg` by default uses the faster DCT method, sacrificing a
little fidelity for improved speed. One can revert to the old behavior by
specifying the attribute `dct_method='INTEGER_ACCURATE'`. * `tf.complex_abs` has
been removed from the Python interface. `tf.abs` supports complex tensors and