WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

-   type: input id: Compiler attributes: label: GCC/Compiler version
    description: if compiling from source placeholder: validations: required:
    false

-   type: input id: Cuda attributes: label: CUDA/cuDNN version description:
    placeholder: validations: required: false

-   type: input id: Gpu attributes: label: GPU model and memory description: if

# Config setting that is satisfied when TensorFlow is being built with CUDA
# support through e.g. `--config=cuda` (or `--config=cuda_clang` in OSS).
alias(
    name = "is_cuda_enabled",
    actual = if_oss(
        "@local_config_cuda//:is_cuda_enabled",
        "@local_config_cuda//cuda:using_config_cuda",
    ),
)

# Config setting that is satisfied when CUDA device code should be compiled

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64
# Note: this is not set to "--cpu", because that changes the package name
# to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow"
# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest

      description: If compiling from source
  - type: input
    id: Compiler
    attributes:
      label: GCC/compiler version
      description: If compiling from source
  - type: input
    id: Cuda
    attributes:
      label: CUDA/cuDNN version
  - type: input
    id: Gpu
    attributes:
      label: GPU model and memory
      description: If compiling from source
  - type: textarea
    id: what-happened

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

1. Creating a PR and observing the presubmit test results

load("@python//:defs.bzl", "compile_pip_requirements")
load("@python_version_repo//:py_version.bzl", "REQUIREMENTS")

# TODO(ybaturina): Remove once TF is migrated to CUDA 12.9.
genrule(
    name = "nvidia_constraints",
    srcs = ["nvidia-requirements.txt"],
    outs = ["nvidia-constraints.txt"],
    cmd = """sed -E "s/>=/==/" $(location nvidia-requirements.txt) > $@;""",
)

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    #   -r ci/official/requirements_updater/nvidia-constraints.txt
    #   -r ci/official/requirements_updater/nvidia-requirements.txt
    #   nvidia-cudnn-cu12
    #   nvidia-cusolver-cu12
nvidia-cuda-cupti-cu12==12.5.82 \
    --hash=sha256:4f835281cf492e2bedd153f5c3de9da8f1d775a419468305e64ce73b3b0c6dc3 \
    --hash=sha256:bde77a5feb66752ec61db2adfe47f56b941842825b4c7e2068aff27c9d107953 \

    #   -r ci/official/requirements_updater/nvidia-constraints.txt
    #   -r ci/official/requirements_updater/nvidia-requirements.txt
    #   nvidia-cudnn-cu12
    #   nvidia-cusolver-cu12
nvidia-cuda-cupti-cu12==12.5.82 \
    --hash=sha256:4f835281cf492e2bedd153f5c3de9da8f1d775a419468305e64ce73b3b0c6dc3 \
    --hash=sha256:bde77a5feb66752ec61db2adfe47f56b941842825b4c7e2068aff27c9d107953 \