```
    cd ci/official/wheel_test
    ```
2.  Run a script for creating requirements file:
    ```
    bash update_requirements.sh <path_to_tensorflow_wheel> <python_version>
    e.g.:
    bash update_requirements.sh /tmp/tensorflow-2.14.0-cp311-cp311-linux_x86_64.whl 3_11
    ```

#### Requirements Updater Script
This script automates the process of updating TensorFlow requirements for a
specific Python version.

# This env is only valid when RBE is available on the selected platform, since
# it enables a derived --config setting.  If RBE is not available (i.e. there
# is no --config setting), bazel would fail and quit. This script does a quick
# check This script checks for such errors early
if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then
  cat <<EOF
ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable.
       TFCI: $TFCI

build/installation issues on GitHub.

-   type: markdown
    attributes:
      value: |

To regenerate them, run the script in this directory, `update_cpp_ops.sh`, with
no arguments. This script will overwrite the existing ops in-place at
***tensorflow/c/experimental/ops/\*_ops.{cc,h}***.

Run this `update_cpp_ops.sh` script when Op definitions change in the registry.

To generate additional operators, extend the lists in this script. Note that

against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

# Here is a single-line example of running a script on Linux to build the
# GPU version of TensorFlow for Python 3.12, using the public TF bazel cache and
# a local build cache:

   `release_updater.sh`

5) Run the `updater.sh` shell script. \
   If the base requirements file hasn't yet been updated to account for the new
   Python version, which will require different versions for at least some
   dependencies, it will need to be updated now, for the script to run
   successfully.

6) A new `requirements_lock_3_11.txt` file should appear under the root of the

        "@com_google_absl//absl/strings",
        "@local_xla//xla/tsl/c:tsl_status_internal",
    ],
    alwayslink = 1,
)

exports_files(
    [
        "version_script.lds",
        "exported_symbols.lds",
    ],
    visibility = ["//visibility:public"],
)

filegroup(
    name = "checkpoint_reader_hdrs",
    srcs = [
        "checkpoint_reader.h",

        # "//tools/cpp:asan_build": [
        # "-Wl,--version-script,$(location //tensorflow/c:version_script.lds)",
        # ],
        # "//tools/cpp:hwasan_build": [
        # "-Wl,--version-script,$(location //tensorflow/c:version_script.lds)",
        # ],
        # "//tools/cpp:msan_build": [
        # "-Wl,--version-script,$(location //tensorflow/c:version_script.lds)",
        # ],
        # "//tools/cpp:tsan_build": [

        dataset/generator/keras sequence.
*   `tf.lite`
    *   Add `GATHER` support to NN API delegate.
    *   tflite object detection script has a debug mode.
    *   Add delegate support for `QUANTIZE`.
    *   Added evaluation script for COCO minival.
    *   Add delegate support for `QUANTIZED_16BIT_LSTM`.
    *   Converts hardswish subgraphs into atomic ops.

}

# This test ensures that all the targets built into the Python package include
# their dependencies. It's a rewritten version of the "smoke test", an older
# Python script that was very difficult to understand. See
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/pip_smoke_test.py
@test "Pip package includes all required //tensorflow dependencies" {