* it is returned directly. Otherwise, a new array of size {@code minLength + padding} is
   * returned, containing the values of {@code array}, and zeroes in the remaining places.
   *
   * @param array the source array
   * @param minLength the minimum length the returned array must guarantee
   * @param padding an extra amount to "grow" the array by if growth is necessary

// Create a new TFE_TensorHandle with the same contents as 'h' but placed
// in the memory of the device name 'device_name'.
// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least
// one of the source or destination devices to be CPU (i.e., for the source or
// destination tensor to be placed in host memory).

  fi
fi

AR_IMAGE_PATH="us-central1-docker.pkg.dev/tensorflow-sigs/tensorflow/build-arm64"

# Build for both JAX and TF usage.  We do these in one place because they share
# almost all of the same cache layers
export DOCKER_BUILDKIT=1
for target in jax tf; do
  AR_IMAGE="$AR_IMAGE_PATH:$target-$TAG"
  docker pull "$AR_IMAGE" || true

)

func TestApplyNewerNoncurrentVersionsLimit(t *testing.T) {
	objAPI, disks, err := prepareErasure(context.Background(), 8)
	if err != nil {
		t.Fatalf("Failed to initialize object layer: %v", err)
	}
	defer removeRoots(disks)
	setObjectLayer(objAPI)
	globalBucketMetadataSys = NewBucketMetadataSys()
	globalBucketObjectLockSys = &BucketObjectLockSys{}
	globalBucketVersioningSys = &BucketVersioningSys{}

   * overkill for most Atomics because they are usually irregularly
   * scattered in memory and thus don't interfere much with each
   * other. But Atomic objects residing in arrays will tend to be
   * placed adjacent to each other, and so will most often share
   * cache lines (with a huge negative performance impact) without
   * this precaution.
   *
   * In part because Cells are relatively large, we avoid creating

		bugLogIf(ctx, fmt.Errorf("metacache.delete: bucket (%s) or id (%s) empty", m.bucket, m.id))
		return
	}
	objAPI := newObjectLayerFn()
	if objAPI == nil {
		internalLogIf(ctx, errors.New("metacache.delete: no object layer"))
		return
	}
	ez, ok := objAPI.(deleteAllStorager)
	if !ok {
		bugLogIf(ctx, errors.New("metacache.delete: expected objAPI to be 'deleteAllStorager'"))
		return
	}

@Experimental
@Immutable
public interface Dependency extends Artifact {
    /**
     * {@return the type of the dependency}
     * A dependency can be a <abbr>JAR</abbr> file,
     * a modular-<abbr>JAR</abbr> if it is intended to be placed on the module-path,
     * a <abbr>JAR</abbr> containing test classes, <i>etc.</i>
     *
     * @see DependencyCoordinates#getType()
     */
    @Nonnull
    Type getType();

    /**

namespace tensorflow {
namespace parallel_device {

// Allocate a parallel device named `device_name` which forwards operations to
// `underlying_devices`, maintaining "parallel tensors" with components placed
// on each underlying device.
//
// For example if `device_name` is
//   "/job:localhost/replica:0/task:0/device:CUSTOM:0"
// and `underlying_devices` is
//   {"/job:localhost/replica:0/task:0/device:GPU:0",

# Query Parameter Models

If you have a group of **query parameters** that are related, you can create a **Pydantic model** to declare them.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Query Parameters with a Pydantic Model

const (
	tierFVID     = "tier-free-versionID"
	tierFVMarker = "tier-free-marker"
	tierSkipFVID = "tier-skip-fvid"
)

// SetTierFreeVersionID sets free-version's versionID. This method is used by
// object layer to pass down a versionID to set for a free-version that may be
// created.
func (fi *FileInfo) SetTierFreeVersionID(versionID string) {
	if fi.Metadata == nil {
		fi.Metadata = make(map[string]string)
	}