: f_(std::forward<G>(f)) {}  // NOLINT(build/c++11)

  Cleanup(Cleanup&& src)  // NOLINT
      : released_(src.is_released()), f_(src.release()) {}

  // Implicitly move-constructible from any compatible Cleanup<G>.
  // The source will be released as if src.release() were called.
  // A moved-from Cleanup can be safely destroyed or reassigned.
  template <typename G>
  Cleanup(Cleanup<G>&& src)  // NOLINT

  virtual const string& Name() const = 0;

  // Returns the operation's device name.
  //
  // The value returned may be different from the one set by SetDeviceName, but
  // it will be compatible with it: the name will be updated by device placement
  // logic to refer to the specific device chosen.
  //
  // Example: If one calls `op->SetDeviceName("/device:GPU")`, the value

COPY builder.packages.txt builder.packages.txt
RUN /setup.packages.sh /builder.packages.txt
RUN update-ca-certificates
# Install devtoolset-9 in /dt10 with glibc 2.17 and libstdc++ 4.8, for building
# manylinux2014-compatible packages.
COPY builder.devtoolset/fixlinks_aarch64.sh /fixlinks.sh
COPY builder.devtoolset/rpm-patch.sh /rpm-patch.sh
COPY builder.devtoolset/build_devtoolset.sh /build_devtoolset.sh

  ;;
devtoolset-10)
  LIBSTDCXX_VERSION="6.0.28"
  LIBSTDCXX_ABI="new"
  ;;
*)
  echo "Usage: $0 {devtoolset-9|devtoolset-10} <target-directory> <arch>"
  echo "Use 'devtoolset-9' to build a manylinux2014 compatible toolchain"
  exit 1
  ;;
esac

mkdir -p "${TARGET}"

mkdir -p ${TARGET}/usr/include

# Put the current kernel headers from ubuntu in place.
ln -s "/usr/include/linux" "${TARGET}/usr/include/linux"

   -----------------------------------------------------------------------------------------------
   
   **1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
   
   
   Make sure you are using compatible TF and CUDA versions.
   Please refer following TF version and CUDA version compatibility table.
   
   | TF  | CUDA |
   
   | :-------------: | :-------------: |
   
   | 2.5.0  | 11.2 |
   

    and
    [tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python).
    TensorFlow has passed version 1.0 and hence cannot make
    non-backward-compatible API changes without a major release. Reviewers of
    your pull request will comment on any API compatibility issues
    [following API review practices](https://github.com/tensorflow/community/blob/master/governance/api-reviews.md).

        checkpoint with a root object. This allows users to create a
        `Checkpoint` object that is compatible with Keras `model.save_weights()`
        and `model.load_weights`. The checkpoint is also compatible with the
        checkpoint saved in the `variables/` folder in the SavedModel.
    *   When restoring, `save_path` can be a path to a SavedModel. The function

/// Whenever we do these changes, the ABI number must be increased.
///
/// Next section will detail how this metadata is used at plugin registration to
/// only load compatible plugins and discard all others.

// LINT.IfChange(random_access_file_ops_version)
constexpr int TF_RANDOM_ACCESS_FILE_OPS_API = 0;
constexpr int TF_RANDOM_ACCESS_FILE_OPS_ABI = 0;

    "@local_config_nccl//:__subpackages__",
    "@local_config_rocm//:__subpackages__",
    "@local_config_tensorrt//:__subpackages__",
    "@local_execution_config_platform//:__subpackages__",
    "@mkl_dnn_acl_compatible//:__subpackages__",
    "@ml_dtypes//:__subpackages__",
    "@nccl_archive//:__subpackages__",
    "@nvtx_archive//:__subpackages__",
    "@onednn//:__subpackages__",
    "@org_sqlite//:__subpackages__",

# This is the same as the official TensorFlow builds.
# See https://developer.nvidia.com/cuda-gpus#compute
# `compute_XY` enables PTX embedding in addition to SASS. PTX
# is forward compatible beyond the current compute capability major
# release while SASS is only forward compatible inside the current
# major release. Example: sm_80 kernels can run on sm_89 GPUs but
# not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.