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.

{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

*   `tf.experimental.dtensor`

    *   Deprecated `dtensor.run_on` in favor of `dtensor.default_mesh` to correctly indicate that the context does not override the mesh that the ops and functions will run on, it only sets a fallback default mesh.

                                               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`.

vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.
Eager mode lets users write imperative-style statements that can be easily
inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape

build:android_x86_64 --fat_apk_cpu=x86_64

# Build everything statically for Android since all static libs are later
# bundled together into a single .so for deployment.
build:android --dynamic_mode=off

# Sets the default Apple platform to macOS.
build:macos --apple_platform_type=macos

# gRPC on MacOS requires this #define
build:macos --copt=-DGRPC_BAZEL_BUILD

# Avoid hitting command line argument limit

    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

  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

[tools](https://www.tensorflow.org/resources/tools),
[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within