# Environment variables to prevent auto-conversion of Linux-like paths to Windows paths.
# This is necessary as some paths end up invalid/mangled.
ENV MSYS_NO_PATHCONV 1
ENV MSYS2_ARG_CONV_EXCL *

# This should only be necessary if there are multiple, differently-versioned
# MSVC compilers installed, and a particular one should be used.
# To find exact versions available:

                model,
                performanceTests,
            )
        performanceTests.forEach { performanceTestTrigger ->
            // The space removal is necessary - otherwise it doesn't show
            val artifactDirName = performanceTestTrigger.name.replace(" ", "")
            performancePartialTrigger.dependencies {
                artifacts(performanceTestTrigger) {

# exec".
# Important: "tfrun foo | bar" is "( tfrun foo ) | bar", not "tfrun (foo | bar)".
# Therefore, "tfrun" commands cannot include pipes -- which is
# probably for the better. If a pipe is necessary for something, it is probably
# complex. Write a well-documented script under utilities/ to encapsulate the
# functionality instead.
tfrun() { "$@"; }

if [[ $(uname -s) = MSYS_NT* ]]; then

# it. TFCI Mac VMs only have one Python version installed so we need to install
# the other versions manually.
if [[ "${TFCI_MACOS_PYENV_INSTALL_ENABLE}" == 1 ]]; then
  # Install the necessary Python, unless it's already present
  pyenv install -s "$TFCI_PYTHON_VERSION"
  pyenv local "$TFCI_PYTHON_VERSION"
  # Do a sanity check to make sure that we using the correct Python version
  python --version
fi

    private UrlFilter urlFilter;
    private StandardCrawlerContainer container;

    @Override
    protected void setUp() throws Exception {
        super.setUp();
        // Initialize container with necessary components
        container = new StandardCrawlerContainer().singleton("dataHelper", MemoryDataHelper.class)
                .singleton("urlFilterService", UrlFilterServiceImpl.class)

  | sort -u
}

# Verify that, given a build target and a license-list generator target, all of
# the dependencies of that target which include a license notice file are then
# included when generating that license. Necessary because the license targets
# in TensorFlow are manually enumerated rather than generated automatically.
do_external_licenses_check(){
  BUILD_TARGET="$1"
  LICENSES_TARGET="$2"

     * to the specified maximum length.
     *
     * @param text the input text to be encoded
     * @return the encoded string, truncated if necessary
     */
    public static String createSuggestTextId(final String text) {
        final String id = encoder.encodeToString(text.getBytes(CoreLibConstants.CHARSET_UTF_8));
        if (id.length() > 445) {

//   function.  This deallocation function will point to client code
//   for tensors populated by the client.  So the client can do things
//   like shadowing a numpy array.
// * We do not provide TF_OK since it is not strictly necessary and we
//   are not optimizing for convenience.
// * We make assumption that one session has one graph.  This should be
//   fine since we have the ability to run sub-graphs.

            StringUtil.EMPTY//
    };

    /** SSL socket factory for HTTPS connections */
    protected LayeredConnectionSocketFactory sslSocketFactory;

    /**
    * Initializes the HTTP client with the necessary configurations and settings.
    * This method sets up the request configurations, authentication schemes,
    * user agent, proxy settings, request headers, cookie store, and connection manager.

`tensorflow` pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run `pip install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with...