*/
@SuppressSignatureCheck
class YubikeyClientAuth {
  fun run() {
    // The typical PKCS11 slot, may vary with different hardware.
    val slot = 0

    val config = "--name=OpenSC\nlibrary=/Library/OpenSC/lib/opensc-pkcs11.so\nslot=$slot\n"

    // May fail with ProviderException with root cause like
    // sun.security.pkcs11.wrapper.PKCS11Exception: CKR_SLOT_ID_INVALID

    void cacheCapacityAndRetrievalOrder() {
        BufferCacheImpl impl = new BufferCacheImpl(2, 3);
        byte[] a = new byte[1];
        byte[] c = new byte[2];
        byte[] d = new byte[4];

        impl.releaseBuffer(a); // goes to slot 0
        impl.releaseBuffer(c); // goes to slot 1
        impl.releaseBuffer(d); // dropped (cache full)

element(s)",badCreditCard:"El número de targeta de crèdit proporcionat no és vàlid",badCVV:"CVV proporcionat no és vàlid",wrongFileDim:"Les dimensions de la imatge no són vàlides,",imageTooTall:"l'alçada de la imatge no pot ser major a",imageTooWide:"l'amplada de la imatge no pot ser major a",imageTooSmall:"la imatge és massa petita",min:"min.",max:"màx.",imageRatioNotAccepted:"La proporció de la imatge (alçada x amplada) no és vàlida"}})}(a,window)});...

    assertThrows(IndexOutOfBoundsException.class, () -> sink.putBytes(new byte[8], 0, -1));
  }

  /**
   * This test creates a long random sequence of inputs, then a lot of differently configured sinks
   * process it; all should produce the same answer, the only difference should be the number of
   * process()/processRemaining() invocations, due to alignment.
   */

  ls -t *.whl | tail -n +2 | xargs mv -t $TFCI_OUTPUT_DIR/extra_wheels
fi

# Repair wheels with auditwheel and delete the old one.
if [[ "$TFCI_WHL_AUDIT_ENABLE" == "1" ]]; then
  python3 -m auditwheel repair --plat "$TFCI_WHL_AUDIT_PLAT" --wheel-dir . *.whl
  # if the wheel is already named correctly, auditwheel won't rename it. so we
  # list all .whl files by their modification time (ls -t) and delete anything

You can use this template to get started, as it includes a lot of the initial set up, security, database and some API endpoints already done for you.

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

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pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder

In a typical modern infrastructure deployment, application, database, key-store, etc. already live in containers and are managed by orchestration platforms. MinIO brings robust, scalable, AWS S3 compatible object storage to the lot.