package plugin

import (
	"fmt"

	"github.com/containernetworking/plugins/pkg/ns"

	"istio.io/istio/pkg/log"
	"istio.io/istio/tools/istio-iptables/pkg/cmd"
	"istio.io/istio/tools/istio-iptables/pkg/config"
	"istio.io/istio/tools/istio-iptables/pkg/dependencies"
)

// getNs is a unit test override variable for interface create.
var getNs = ns.GetNS

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

/tools/                                                          @istio/wg-test-and-release-maintainers
/tools/packaging/common/envoy_bootstrap.json                     @istio/wg-networking-maintainers
/tools/istio-iptables/                                           @istio/wg-networking-maintainers
/tools/istio-clean-iptables/                                     @istio/wg-networking-maintainers

      # Run once a week on Sunday at midnight. See http://crontab.guru
      - cron: '0 0 * * 0'
  push:
    paths:
      - '.github/workflows/sigbuild-docker.yml'
      - 'tensorflow/tools/tf_sig_build_dockerfiles/**'
      - '!tensorflow/tools/tf_sig_build_dockerfiles/README.md'
    branches:
      - master

permissions:
  contents: read

jobs:
  docker:

## Benchmarks und Geschwindigkeit

Wenn Sie sich die Benchmarks ansehen, werden häufig mehrere Tools mit unterschiedlichen Eigenschaften als gleichwertig verglichen.

Konkret geht es darum, Uvicorn, Starlette und FastAPI miteinander zu vergleichen (neben vielen anderen Tools).

## Intro

**FastAPI** wouldn't exist if not for the previous work of others.

There have been many tools created before that have helped inspire its creation.

I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.

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

on:
  pull_request:
    types: [labeled, opened, synchronize, reopened]
    paths:
      - '.github/workflows/sigbuild-docker-presubmit.yml'
      - 'tensorflow/tools/tf_sig_build_dockerfiles/**'
      - '!tensorflow/tools/tf_sig_build_dockerfiles/README.md'

permissions:
  contents: read

jobs:
  docker:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks

  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: