"tf_cc_test",
    "tf_copts",
    "tf_cuda_cc_test",
    "tf_cuda_library",
)
load("//tensorflow:tensorflow.default.bzl", "cc_header_only_library", "filegroup")
load(
    "//tensorflow/core/platform:build_config_root.bzl",
    "tf_cuda_tests_tags",
)
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],

    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 ]]
}

teardown_file() {
    bazel shutdown

If these test dependencies need to be in the TensorFlow pip package, please
add them to //tensorflow/tools/pip_package/BUILD. Otherwise, add the no_pip tag
to the test, or change code_check_full.bats in the SIG Build repository. That's
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats
Here are the affected tests:
EOF
    while read dep; do

#
#   Bazel will cache your builds in tensorflow/build_output/cache,
#   and will also try using public build cache results to speed up
#   your builds. This usually saves a lot of time, especially when
#   re-running tests. However, note that:
#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures
#      or tests passing incorrectly.

labels: 'TFLiteConverter'

---

### 1. System information

- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):

### 2. Code

Provide code to help us reproduce your issues using one of the following options:

#### Option A: Reference colab notebooks

A. Unique TensorFlower <******@****.***> 1693505552 -0700

labels: 'comp:lite-in-play-services'

---

**System information**
- Android Device information (use `adb shell getprop ro.build.fingerprint`
  if possible):
- TensorFlow Lite in Play Services SDK version (found in `build.gradle`):
- Google Play Services version
  (`Settings` > `Apps` > `Google Play Services` > `App details`):

**Standalone code to reproduce the issue**

   actually using the new version with Tensorflow.

2) In the `WORKSPACE` file, add the new version to the `python_versions`
   parameter of the `python_register_multi_toolchains` function.

3) In the `BUILD.bazel` file, add a load statement for the new version, e.g.

   ```
      load("@python//3.11:defs.bzl",
           compile_pip_requirements_3_11 = "compile_pip_requirements")
   ```

        `HardSwish`.

### Packaging Support

*   Added `tf.sysconfig.get_build_info()`. Returns a dict that describes the
    build environment of the currently installed TensorFlow package, e.g. the
    NVIDIA CUDA and NVIDIA CuDNN versions used when TensorFlow was built.

### Profiler

*   Fix a subtle use-after-free issue in `XStatVisitor::RefValue()`.

### TPU Enhancements

variables from your model are also going to be untrusted. That means that if
your code interacts with the filesystem, network, etc. and uses checkpointed
variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.

### Running a TensorFlow server