_DEFAULT_PROMPT_ASK_ATTEMPTS = 10

_TF_BAZELRC_FILENAME = '.tf_configure.bazelrc'
_TF_WORKSPACE_ROOT = ''
_TF_BAZELRC = ''
_TF_CURRENT_BAZEL_VERSION = None

NCCL_LIB_PATHS = [
    'lib64/', 'lib/powerpc64le-linux-gnu/', 'lib/x86_64-linux-gnu/', ''
]

# List of files to configure when building Bazel on Apple platforms.
APPLE_BAZEL_FILES = [
    'tensorflow/lite/ios/BUILD', 'tensorflow/lite/objc/BUILD',
    'tensorflow/lite/swift/BUILD',

      echo "Supported platform(s): linux." >&2
      echo "Bailing out." >&2
      exit 2
  esac

  HOST_ARCH=${HOST_ARCH_OVERRIDE:-"$(uname -m)"}
  case "${HOST_ARCH}" in
    x86_64*|i?86_64*|amd64*)
      HOST_ARCH="amd64"
      ;;
    aHOST_arch64*|aarch64*|arm64*)
      HOST_ARCH="arm64"
      ;;
    *)
      echo "Unknown, unsupported architecture (${HOST_ARCH})." >&2

// gccMachine returns the gcc -m flag to use, either "-m32", "-m64" or "-marm".
func (p *Package) gccMachine() []string {
	switch goarch {
	case "amd64":
		if goos == "darwin" {
			return []string{"-arch", "x86_64", "-m64"}
		}
		return []string{"-m64"}
	case "arm64":
		if goos == "darwin" {
			return []string{"-arch", "arm64"}
		}
	case "386":
		return []string{"-m32"}
	case "arm":

func (b *Builder) gccArchArgs() []string {
	switch cfg.Goarch {
	case "386":
		return []string{"-m32"}
	case "amd64":
		if cfg.Goos == "darwin" {
			return []string{"-arch", "x86_64", "-m64"}
		}
		return []string{"-m64"}
	case "arm64":
		if cfg.Goos == "darwin" {
			return []string{"-arch", "arm64"}
		}
	case "arm":
		return []string{"-marm"} // not thumb
	case "s390x":

00000040  c3 e1 8e 89 20 a5 2d 0b  31 b5 e0 16 54 ce 93 9b  |.... .-.1...T...|
00000050  de cc b1 af 48 48 33 96  4d a6 00 78 7b 60 3f 7c  |....HH3.M..x{`?||
00000060  cd 86 64 5e 38 e8 fd 60  d4 b1 89 cd e4 fb 42 b5  |..d^8..`......B.|
00000070  82 8f e5 23 91 87 6d 54  9e 85 37 34 df ae 70 a5  |...#..mT..74..p.|
00000080  cd 4c de 95 f7 9f a9 b4  87 0b 16 03 03 02 69 d1  |.L............i.|

        instead of 2.15, or users could receive an installation error due to
        those missing dependencies.
*   TensorFlow 2.15.0.post1 is being released for Linux x86_64 to resolve this
    issue as quickly as possible.
    *   This version removes the `tensorrt` Python package dependencies from the
        tensorflow[and-cuda] installation method to ensure `pip install

// copies of these functions for use with other sizes.
// Specifically:
//
// - A function with a name ending in _8_32 takes []byte and []int32 arguments
//   and is duplicated into _32_32, _8_64, and _64_64 forms.
//   The _32_32 and _64_64_ suffixes are shortened to plain _32 and _64.
//   Any lines in the function body that contain the text "byte-only" or "256"
//   are stripped when creating _32_32 and _64_64 forms.

		if z.Op != OpPPC64MOVHZreg {
			break
		}
		x := z.Args[0]
		if !(c < 16 && z.Uses == 1) {
			break
		}
		v.reset(OpPPC64CLRLSLDI)
		v.AuxInt = int32ToAuxInt(newPPC64ShiftAuxInt(c, 48, 63, 64))
		v.AddArg(x)
		return true
	}
	// match: (SLDconst [c] z:(MOVWZreg x))
	// cond: c < 32 && z.Uses == 1
	// result: (CLRLSLDI [newPPC64ShiftAuxInt(c,32,63,64)] x)
	for {