If you use any other tool to manage your installations, like `uv`, Poetry, Pipenv, or others, they all have a way that you can use to define specific versions for your packages.

## Available versions { #available-versions }

You can see the available versions (e.g. to check what is the current latest) in the [Release Notes](../release-notes.md){.internal-link target=_blank}.

      "keras.api._v1.keras.__internal__.legacy.rnn_cell")
_current_module.nn.__path__ = [_module_dir] + _current_module.nn.__path__

del importlib

# Load all plugin libraries from site-packages/tensorflow-plugins if we are
# running under pip.
# TODO(gunan): Find a better location for this code snippet.
from tensorflow.python.framework import load_library as _ll
from tensorflow.python.lib.io import file_io as _fi

And dependencies can be created in a very simple and intuitive way that allows you to just import the Python packages you need, and integrate them with your API functions in a couple of lines of code, *literally*.

You will see examples of this in the next chapters, about relational and NoSQL databases, security, etc.

    for the required packages. Alternatively, use the said
    [tensorflow/build Docker images](https://hub.docker.com/r/tensorflow/build)
    (`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` are no
    longer supported for development). Use TF SIG Build Dockerfiles in
    development to avoid installing the packages directly on your system (in

argument to the bazel command:

```
bazel run //ci/official/requirements_updater:requirements.update --repo_env=HERMETIC_PYTHON_VERSION=3.12 -- --pre
```

If you need to upgrade all of the packages in requirements lock file, just pass
the `--upgrade` parameter:

```
bazel run //ci/official/requirements_updater:requirements.update --repo_env=HERMETIC_PYTHON_VERSION=3.12 -- --upgrade
```

import junit.framework.TestCase;
import org.jspecify.annotations.NullUnmarked;

/**
 * Tests that all package-private {@code writeReplace} methods are overridden in any existing
 * subclasses. Without such overrides, optimizers might put a {@code writeReplace}-containing class
 * and its subclass in different packages, causing the serialization system to fail to invoke {@code

            <_fixupmessages>^Classes found in the wrong directory: .*</_fixupmessages>
            <Export-Package>
              !com.google.common.base.internal,
              !com.google.common.util.concurrent.internal,
              !META-INF.*,
              com.google.common.*
            </Export-Package>
            <Import-Package>
              com.google.common.util.concurrent.internal,

     * because a name may be present even if the descriptor is absent. This method is for
     * cases when more information is desired, such as the set of exported packages.</p>
     *
     * @param dependency path to the dependency for which to get the module name
     * @return module name of the dependency at the given path, or empty if the dependency is not modular

        "//waymo/ml/models/...",
    ],
)

package_group(
    name = "ndarray_tensor_allow_list",
    packages = [
        "//learning/gemini/gemax/...",
        "//third_party/py/courier/...",
        "//third_party/py/tensorfn/...",
    ],
)

# Packages that use private types symbols, until they are exported.
# TODO(b/154650521) Remove.

Und Abhängigkeiten können auf sehr einfache und intuitive Weise erstellt werden, sodass Sie einfach die benötigten Python-Packages importieren und sie in wenigen Codezeilen, *im wahrsten Sinne des Wortes*, mit Ihren API-Funktionen integrieren.

Beispiele hierfür finden Sie in den nächsten Kapiteln zu relationalen und NoSQL-Datenbanken, Sicherheit usw.