w < 2 { panic("w must be at least 2 by the definition of NAF") } else if w > 8 { panic("NAF digits must fit in int8") } var naf [256]int8 var digits [5]uint64 for i := 0; i < 4; i++ { digits[i] = byteorder.LEUint64(b[i*8:]) } width := uint64(1 << w) windowMask := uint64(width - 1) pos := uint(0) carry := uint64(0) for pos < 256 { indexU64 := pos / 64 indexBit := pos % 64 var bitBuf uint64 if indexBit < 64-w { // This window's bits are contained in a single u64 bitBuf = digits[indexU64] >> indexBit...

*   `tf.scatter_update` now no longer guarantees that lexicographically largest
    index be used for update when duplicate entries exist.
*   `tf.image.random_crop(image, [height, width])` is now `tf.random_crop(image,
    [height, width, depth])`, and `tf.random_crop` works for any rank (not just
    3-D images). The C++ `RandomCrop` op has been replaced with pure Python.