your changes by running the command:

```bash
tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the

import javax.lang.model.SourceVersion;
import javax.lang.model.element.Element;
import javax.lang.model.element.PackageElement;
import javax.lang.model.element.TypeElement;
import javax.tools.Diagnostic;
import javax.tools.FileObject;
import javax.tools.StandardLocation;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.io.PrintWriter;
import java.io.StringWriter;

  popd
  cp "${tmp_dir}/new.jar" "${dest_jar}"
  rm -rf "${tmp_dir}"
}
DIR=$1
TARBALL_SUFFIX=$2
mkdir -p "$DIR"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz "${DIR}/libtensorflow${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz "${DIR}/libtensorflow_jni${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/java/libtensorflow.jar "${DIR}"

name: Upload SIG Build docker containers modified for release branches

on:
  workflow_dispatch:
  push:
    paths:
      - '.github/workflows/sigbuild-docker-branch.yml'
      - 'tensorflow/tools/tf_sig_build_dockerfiles/**'
      - '!tensorflow/tools/tf_sig_build_dockerfiles/README.md'
    branches:
      - "r[1-9].[0-9]+"

permissions:
  contents: read

jobs:
  docker:

              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.file=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.main=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.model=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.parser=ALL-UNNAMED</arg>

              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.file=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.main=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.model=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.parser=ALL-UNNAMED</arg>

# git history files
.history_rewritten_*
# Eclipse artifacts
.project
.pydevproject
#Vagrant
tools/vagrant/.vagrant/
# Intellij
*.iml
.idea/
.run/
# Visual Studio Code
.vscode/
# Bazel
/bazel-*
# vi swap files
.*.swp
# vi backups
*.bak
# common backups
*~
# python artifacts
*.pyc
# pilot
pilot/pkg/kube/config
pilot/pkg/proxy/envoy/envoy
# lint
lintconfig.gen.json
.istiorc
.istiorc.mk

  popd
  cp "${tmp_dir}/new.jar" "${dest_jar}"
  rm -rf "${tmp_dir}"
}
DIR=$1
TARBALL_SUFFIX=$2
mkdir -p "$DIR"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz "${DIR}/libtensorflow${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz "${DIR}/libtensorflow_jni${TARBALL_SUFFIX}.tar.gz"
cp bazel-bin/tensorflow/java/libtensorflow.jar "${DIR}"

But in most of the cases where we need to do something like this, we want the model just to **filter/remove** some of the data as in this example.

And in those cases, we can use classes and inheritance to take advantage of function **type annotations** to get better support in the editor and tools, and still get the FastAPI **data filtering**.

## Deployment-Strategien

Abhängig von Ihrem spezifischen Anwendungsfall und den von Ihnen verwendeten Tools gibt es mehrere Möglichkeiten, das zu tun.

Sie könnten mithilfe einer Kombination von Tools selbst **einen Server bereitstellen**, Sie könnten einen **Cloud-Dienst** nutzen, der einen Teil der Arbeit für Sie erledigt, oder andere mögliche Optionen.