:
#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
        api_def_map(op_list),
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
        update_docs_called(false) {
  }

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
  tensorflow::ApiDefMap api_def_map TF_GUARDED_BY(lock);
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

// TODO(sjr): Remove the IS_MOBILE_PLATFORM guard. This will require ensuring
// that the tensorflow/cc/saved_model:loader build target is mobile friendly.
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "Loading a SavedModel is not supported on mobile. File a bug at "

-   type: input id: OS attributes: label: OS Platform and Distribution
    description: placeholder: e.g., Linux Ubuntu 16.04 validations: required:
    false

-   type: input id: Mobile attributes: label: Mobile device description:
    placeholder: e.g., Linux Ubuntu 16.04 validations: required: false

-   type: input id: Python attributes: label: Python version description:

    validations:
      required: true
  - type: input
    id: OS
    attributes:
      label: OS platform and distribution
      placeholder: e.g., Linux Ubuntu 16.04
  - type: input
    id: Mobile
    attributes:
      label: Mobile device
      placeholder: e.g., Linux Ubuntu 16.04
  - type: input
    id: Python
    attributes:
      label: Python version
      placeholder: e.g., 3.9
  - type: input
    id: Bazel

  return false;
#else   // !defined(IS_MOBILE_PLATFORM)
  bool is_alive;
  status->status =
      tensorflow::unwrap(ctx)->GetDistributedManager()->CheckRemoteAlive(
          worker_name, &is_alive);
  return is_alive;
#endif  // !IS_MOBILE_PLATFORM
}

TF_CAPI_EXPORT extern void TFE_ContextAsyncWait(TFE_Context* ctx,
                                                TF_Status* status) {

  //===--------------------------------------------------------------------===//
  // Distributed runtime related functions.
  //===--------------------------------------------------------------------===//
#if !defined(IS_MOBILE_PLATFORM)
  // Set up a multi-client distributed execution environment. Must be called on
  // all tasks in the cluster.
  // This call internally coordinates with other tasks to initialize the eager

TF_Library* TF_LoadPluggableDeviceLibrary(const char* library_filename,
                                          TF_Status* status) {
#if defined(IS_MOBILE_PLATFORM) || defined(IS_SLIM_BUILD)
  status->status = tensorflow::errors::Unimplemented(
      "PluggableDevice plugin functionality is not supported on mobile");
  return nullptr;
#else
  TF_Library* lib_handle = new TF_Library;
  static tensorflow::mutex mu(tensorflow::LINKER_INITIALIZED);

init files creation process and is subsequently stored in the wheel file after
the build. It also contains a few paths that cannot be directly imported. These
paths point to attributes or sub-modules within a module's namespace, but they
don't correspond to an actual file or directory on the filesystem. The list of
such paths is stored in the packages_for_skip variable and will be skipped
during the test.

##### How to Build

```

    if [[ ! -s $BATS_TEST_TMPDIR/files ]]; then return 0; fi
    xargs -a $BATS_TEST_TMPDIR/files -n1 -P $(nproc --all) \
        python -m pylint --rcfile=tensorflow/tools/ci_build/pylintrc --score false \
        | grep -v "**** Module" \
        | tee $BATS_TEST_TMPDIR/needs_help.txt
    [[ ! -s $BATS_TEST_TMPDIR/needs_help.txt ]]
}

@test "API compatibility test passes, ensuring no unexpected changes to the TF API" {

// Load the library specified by library_filename and register the pluggable
// device and related kernels present in that library. This function is not
// supported on embedded on mobile and embedded platforms and will fail if
// called.
//
// Pass "library_filename" to a platform-specific mechanism for dynamically
// loading a library. The rules for determining the exact location of the