build --copt="-mtune=generic" --copt="-march=armv8-a" --copt="-O3"

# Test-related settings below this point.
test --build_tests_only --keep_going --test_output=errors --verbose_failures=true
test --test_timeout=500,900,-1,-1
# Give only the list of failed tests at the end of the log
test --test_summary=short

# "nonpip" tests are regular py_test tests.

#   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.
#    - Automatic LLVM updates are known to extend build time even with
#      the cache; this is unavoidable.
export TFCI=py311,linux_x86,public_cache,disk_cache

build --copt="-mtune=generic" --copt="-march=armv8-a" --copt="-O3"

# Test-related settings below this point.
test --build_tests_only --keep_going --test_output=errors --verbose_failures=true
test --test_timeout=500,900,-1,-1
# Give only the list of failed tests at the end of the log
test --test_summary=short

# "nonpip" tests are regular py_test tests.

*   Fixes multiple `CHECK`-failures during Grappler's `IsSimplifiableReshape`
    ([CVE-2022-23581](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23581))
*   Fixes multiple `CHECK`-failures during Grappler's `SafeToRemoveIdentity`
    ([CVE-2022-23579](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23579))
*   Fixes multiple `CHECK`-failures in `TensorByteSize`

build:windows --host_linkopt=/DEBUG
build:windows --linkopt=/OPT:REF
build:windows --host_linkopt=/OPT:REF
build:windows --linkopt=/OPT:ICF
build:windows --host_linkopt=/OPT:ICF

# Verbose failure logs when something goes wrong
build:windows --verbose_failures

# Work around potential issues with large command lines on windows.
# See: https://github.com/bazelbuild/bazel/issues/5163
build:windows --features=compiler_param_file

- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

- Model produces wrong results and/or has lesser accuracy.

      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.