# install it manually. We use Twine in nightly builds to upload Python packages
# to PyPI.
if [[ "$TFCI_MACOS_TWINE_INSTALL_ENABLE" == 1 ]]; then
  pip install twine==3.6.0
fi

# Scheduled nightly and release builds upload build artifacts (Pip packages,
# Libtensorflow archives) to GCS buckets. TFCI Mac VMs need to authenticate as

    # o packageBase: (Required)
    #  The base directory of package for generated class.
    #  The class package is 'this property value + detail package value'.
    #  However, detail packages have default value so basically
    #  you only have to specify the property 'packageBase'.
    #  If this property is specified and detail package properties is not specified, 
    #  Then the packages of generated class are as follows:
    #

      "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

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

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}.

## About versions

# limitations under the License.
# ==============================================================================
#
# setup.python.sh: Install a specific Python version and packages for it.
# Usage: setup.python.sh <pyversion> <requirements.txt>

# Sets up custom apt sources for our TF images.

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

In fast jedem Framework wird die Handhabung der Sicherheit recht schnell zu einem ziemlich komplexen Thema.

Viele Packages, die es stark vereinfachen, müssen viele Kompromisse beim Datenmodell, der Datenbank und den verfügbaren Funktionen eingehen. Und einige dieser Pakete, die die Dinge zu sehr vereinfachen, weisen tatsächlich Sicherheitslücken auf.

---

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

The defaults that will be compiled into a main package
are reported by the command:

{{raw `
	go list -f '{{.DefaultGODEBUG}}' my/main/package
`}}

Only differences from the base Go toolchain defaults are reported.

When testing a package, `//go:debug` lines in the `*_test.go`
files are treated as directives for the test's main package.
In any other context, `//go:debug` lines are ignored by the toolchain;

          </execution>
        </executions>
        <configuration>
          <instructions>
            <Export-Package>
              !com.google.common.base.internal,
              !com.google.common.util.concurrent.internal,
              com.google.common.*
            </Export-Package>
            <Import-Package>
              com.google.common.util.concurrent.internal,
              javax.annotation;resolution:=optional,