TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

  explicit TfDlManagedTensorCtx(const TensorReference& ref) : reference(ref) {}
};

// Gets tensor from eager tensor handle.
const Tensor* GetTensorFromHandle(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;

    change.
-   You make the change and submit it for the review again.
-   This cycle repeats itself until the PR gets approved.
-   Note: As a friendly reminder, we may reach out to you if the PR is awaiting
    your response for more than 2 weeks.

**4. Approved**

-   Once the PR is approved, it gets `kokoro:force-run` label applied and it
    initiates CI/CD tests.
-   We can't move forward if these tests fail.

                                               TF_Status* status);

// Gets the `PJRT_Client*` stored in TF global ResourceManager.
TF_CAPI_EXPORT extern PJRT_Client* TF_GetPjRtCClient(const char* device_type,
                                                     TF_Status* status);

// Gets the `PJRT_Buffer*` stored in the tensor. The status will contain error
// if the tensor does not have a `PjRtCApiBuffer`.

  /// Releases all resources used by the filesystem
  ///
  /// NOTE: TensorFlow does not unload DSOs. Thus, the only way a filesystem
  /// won't be registered anymore is if this function gets called by core
  /// TensorFlow and the `TF_Filesystem*` object is destroyed. However, due to
  /// registration being done in a static instance of `Env`, the destructor of
  /// `FileSystem` is never called (see

            instead of relying on `Model.inputs` and `Model.input_names`, which
            are no longer set for subclass Models. This attr is set in eager,
            `tf.function`, and graph modes. This gets rid of the need for users
            to manually call `Model._set_inputs` when using Custom Training
            Loops(CTLs).
        *   Dynamic shapes are supported for generators by calling the Model on

    test "$WHEEL_MEGABYTES" -le "$LARGEST_OK_SIZE"
}

# Note: this runs before the tests further down the file, so TF is installed in
# the venv and the venv is active when those tests run. The venv gets cleaned
# up in teardown_file() above.
@test "Wheel is installable" {
    python3 -m venv /tf/venv
    source /tf/venv/bin/activate
    python3 -m pip install --upgrade setuptools wheel

// enabled through MarkForCompilationPassFlags.
TF_CAPI_EXPORT unsigned char TF_GetXlaAutoJitEnabled();

// Sets XLA's minimum cluster size. This has global effect.
TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

// Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
// folding. This is for testing to ensure that XLA is being tested rather than
// Tensorflow's CPU implementation through constant folding.

  write_action_env_to_bazelrc('ANDROID_SDK_API_LEVEL', android_api_level)
  write_action_env_to_bazelrc('ANDROID_SDK_HOME', android_sdk_home_path)


def get_ndk_api_level(environ_cp, android_ndk_home_path):
  """Gets the appropriate NDK API level to use for the provided Android NDK path.
  """

  # First check to see if we're using a blessed version of the NDK.
  properties_path = '%s/source.properties' % android_ndk_home_path

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Note: this file gets sourced in utilities/setup.sh, which has "set -u"
# (error on undefined variables). This ensures that (a) every TFCI variable
# has an explicit default value, and (b) no script can accidentally use a

TF_CAPI_EXPORT extern void TF_OperationGetAttrTensorShapeProtoList(
    TF_Operation* oper, const char* attr_name, TF_Buffer** values,
    int max_values, TF_Status* status);

// Gets the TF_Tensor valued attribute of `attr_name` of `oper`.
//
// Allocates a new TF_Tensor which the caller is expected to take
// ownership of (and can deallocate using TF_DeleteTensor).