default_version = default_python_version,
    ignore_root_user_error = True,
    python_versions = [
        "3.9",
        "3.10",
        "3.11",
        "3.12",
    ],
    register_coverage_tool = True,
)

and a requirements_lock file. The requirements_lock file is generated by the
[requirements_updater](https://github.com/tensorflow/tensorflow/tree/master/ci/official/requirements_updater)
tool using the path to this wheel file.

### Hermetic Python

For details about hermetic Python and setting its toolchain version, see

    [Environment]::SetEnvironmentVariable(\"PATH\", $old_path + \";C:\Program Files (x86)\Windows Kits\10\App Certification Kit\", \"Machine\");

# Install WiX toolset (v4) - Necessary for MSI Installer/Signing builds
RUN dotnet tool install --global wix

# Install msys2, packages and add to path.
RUN [Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; \

```

To specify the version via a Bazel command argument, use the following:

```
--repo_env=TF_PYTHON_VERSION=3.11
```

## Requirements updater

Requirements updater is a standalone tool, intended to simplify process of
updating requirements for multiple minor versions of Python.

It takes in a file with a set of dependencies, and produces a more detailed

1. Creating a PR and observing the presubmit test results
2. Running the CI scripts locally, as explained below
3. **Google employees only**: Google employees can use an internal-only tool
called "MLCI" that makes testing more convenient: it can execute any full CI job
against a pending change. Search for "MLCI" internally to find it.

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

    *   Added support for `tflite_convert` command line tool in 2.0.
    *   Post-training quantization tool supports quantizing weights shared by
        multiple operations. The models made with versions of this tool will use
        INT8 types for weights and will only be executable interpreters from
        this version onwards.
    *   Post-training quantization tool supports fp16 weights and GPU delegate

        "//tensorflow/core/grappler/clusters:virtual_cluster",
        "//tensorflow/core/grappler/costs:graph_memory",
        "//tensorflow/core/grappler/graph_analyzer:graph_analyzer_tool",
        "//tensorflow/core/grappler/optimizers:meta_optimizer",
        "//tensorflow/core/grappler:grappler_item",
        "//tensorflow/core/grappler:grappler_item_builder",