raise FileNotFoundError(
        "Can't find 'find_cuda_config.py' script inside working directory")
  proc = subprocess.Popen(
      [environ_cp['PYTHON_BIN_PATH'], paths[0]] + cuda_libraries,
      stdout=subprocess.PIPE,
      env=maybe_encode_env(environ_cp))

  if proc.wait():
    # Errors from find_cuda_config.py were sent to stderr.
    print('Asking for detailed CUDA configuration...\n')
    return False

		}
	}
}

func (p *ReplicationPool) persistToDrive(ctx context.Context, v MRFReplicateEntries) {
	newReader := func() io.ReadCloser {
		r, w := io.Pipe()
		go func() {
			// Initialize MRF meta header.
			var data [4]byte
			binary.LittleEndian.PutUint16(data[0:2], mrfMetaFormat)
			binary.LittleEndian.PutUint16(data[2:4], mrfMetaVersion)

</p>

<pre class="grammar">
"var" IdentifierList = ExpressionList .
</pre>

<pre>
i, j := 0, 10
f := func() int { return 7 }
ch := make(chan int)
r, w, _ := os.Pipe()  // os.Pipe() returns a connected pair of Files and an error, if any
_, y, _ := coord(p)   // coord() returns three values; only interested in y coordinate
</pre>

<p>

</p>

<pre class="grammar">
"var" IdentifierList "=" ExpressionList .
</pre>

<pre>
i, j := 0, 10
f := func() int { return 7 }
ch := make(chan int)
r, w, _ := os.Pipe()  // os.Pipe() returns a connected pair of Files and an error, if any
_, y, _ := coord(p)   // coord() returns three values; only interested in y coordinate
</pre>

<p>

//tensorflow/lite/python:interpreter_test.py
EOF

  # Get the full list of files and targets which get included into the pip
  # package
  bazel cquery --keep_going 'deps(//tensorflow/tools/pip_package:wheel)' | sort -u > $BATS_TEST_TMPDIR/pip_deps
  # Find all Python py_test targets not tagged "no_pip" or "manual", excluding
  # any targets in ignored packages. Combine this list of targets into a bazel

        run: pip install -r requirements-docs.txt
      # Install MkDocs Material Insiders here just to put it in the cache for the rest of the steps
      - name: Install Material for MkDocs Insiders
        if: ( github.event_name != 'pull_request' || github.secret_source == 'Actions' ) && steps.cache.outputs.cache-hit != 'true'
        run: |

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,
    python_version = TF_PYTHON_VERSION,
)

load("@python//:defs.bzl", "interpreter")
load("@rules_python//python:pip.bzl", "pip_parse")

pip_parse(
    name = "pypi",
    python_interpreter_target = interpreter,
    requirements = "//:requirements_lock_" + TF_PYTHON_VERSION.replace(".", "_") + ".txt",
)

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_tpu
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow_tpu"
TFCI_LIB_SUFFIX="-tpu-linux-x86_64"
TFCI_WHL_BAZEL_TEST_ENABLE=0
TFCI_WHL_IMPORT_TEST_ENABLE=0
TFCI_WHL_SIZE_LIMIT=580M

    Die Option `--no-cache-dir` weist `pip` an, die heruntergeladenen Pakete nicht lokal zu speichern, da dies nur benötigt wird, sollte `pip` erneut ausgeführt werden, um dieselben Pakete zu installieren, aber das ist beim Arbeiten mit Containern nicht der Fall.

    !!! note "Hinweis"
        Das `--no-cache-dir` bezieht sich nur auf `pip`, es hat nichts mit Docker oder Containern zu tun.

    The `--no-cache-dir` option tells `pip` to not save the downloaded packages locally, as that is only if `pip` was going to be run again to install the same packages, but that's not the case when working with containers.

    !!! note
        The `--no-cache-dir` is only related to `pip`, it has nothing to do with Docker or containers.

    The `--upgrade` option tells `pip` to upgrade the packages if they are already installed.