class AbstractOpAttrs {
 protected:
  enum AbstractOpAttrsKind { kEager, kTfrt };
  explicit AbstractOpAttrs(AbstractOpAttrsKind kind) : kind_(kind) {}

 public:
  // Returns which subclass is this instance of.
  AbstractOpAttrsKind getKind() const { return kind_; }
  virtual ~AbstractOpAttrs() = default;

  // Returns the AbstractFunction as a FunctionDef.
  virtual void GetNameAttrList(

issue in TensorFlow Lite that is not about Converters, Play Services or Ops
body: - type: dropdown id: issue-type attributes: label: Issue Type description:
What type of issue would you like to report? multiple: false options: - Bug -
Build/Install - Performance - Support - Feature Request - Documentation Feature
Request - Documentation Bug - Others validations: required: true - type:
markdown attributes: value: | Please make sure that this is a bug. As per our

the local wheel file specified in the `requirements_lock_<python_version>.txt`.

Packages are imported one by one in alphabetical order during runtime.

The test doesn't identify package's order-dependent issues; for instance,
importing "tf.foo" followed by "tf.bar" won't reveal that "tf.bar" depends on
"tf.foo" being imported first.

The `_api/v2/api_packages.txt` file is generated during the TensorFlow API v2

FROM ubuntu:20.04 as builder
################################################################################

# Install devtoolset build dependencies
COPY setup.packages.sh setup.packages.sh
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.

    - name: Set up Python 3.9
      uses: actions/setup-python@2c3dd9e7e29afd70cc0950079bde6c979d1f69f9 # v4.3.1
      with:
        python-version: "3.9"
    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint==2.13.9 numpy wheel
    - name: Run PyLint on changed files
      run: |

py_repositories()

load("@rules_python//python:repositories.bzl", "python_register_multi_toolchains")  # buildifier: disable=same-origin-load
load("@rules_python//python/pip_install:repositories.bzl", "pip_install_dependencies")

default_python_version = "3.10"

load(
    "//:updater_config_repository.bzl",
    "updater_config_repository",  # @unused
)

# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn8=8.9.6.50-1+cuda12.2
libcudnn8-dev=8.9.6.50-1+cuda12.2

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

ln -s $(ls /$1/lib) /$1/lib/python3
ln -s ../tensorflow $1/tensorflow
# extglob is necessary for @(a|b) pattern matching
# see "extglob" in the bash manual page ($ man bash)
bash -O extglob -c "/$1/bin/pip install $2"

  // Check if the remote task is alive.
  virtual Status CheckRemoteAlive(const std::string& remote_task_name,
                                  bool* is_alive) = 0;

  // Get pointer to the coordination service agent instance.
  virtual tsl::CoordinationServiceAgent* GetCoordinationServiceAgent() = 0;
};
}  // namespace tensorflow

  // clients from directly destroying this object since it may manage it's own
  // lifetime through ref counting. Thus this must be allocated on the heap and
  // clients MUST call Release() in order to destroy an instance of this class.
  virtual void Release() = 0;

  virtual Status Reset(const char* op, const char* raw_device_name) = 0;

  virtual const string& Name() const = 0;

  // Returns the operation's device name.
  //