* the query result cursor will both be closed, even if the operation is cancelled or fails.
 *
 * <p>Note that if you don't call {@code closeAsync()}, the captured objects will not be closed. The
 * automatic-closing approach described above is safer.
 *
 * @param <V> the type of the value of this step
 * @since 30.0
 */
// TODO(dpb): Consider reusing one CloseableList for the entire pipeline, modulo combinations.

          "nodes after running them or create a new session.");
    }
  }
}

namespace {

// Helper method that creates a shape handle for a shape described by dims.
tensorflow::shape_inference::ShapeHandle ShapeHandleFromDims(
    tensorflow::shape_inference::InferenceContext* ic, int num_dims,
    const int64_t* dims) {
  if (num_dims != -1) {

)

var (
	globalReplicationPool  = once.NewSingleton[ReplicationPool]()
	globalReplicationStats atomic.Pointer[ReplicationStats]
)

// ReplicationPool describes replication pool
type ReplicationPool struct {
	// atomic ops:
	activeWorkers    int32
	activeLrgWorkers int32
	activeMRFWorkers int32

	objLayer    ObjectLayer
	ctx         context.Context

	rt, ok := remoteInstanceTransport.Load().(http.RoundTripper)
	if ok {
		return rt
	}
	return nil
}

// Returns a minio-go Client configured to access remote host described by destDNSRecord
// Applicable only in a federated deployment
var getRemoteInstanceClient = func(r *http.Request, host string) (*miniogo.Core, error) {
	cred := getReqAccessCred(r, globalSite.Region())

 * the query result cursor will both be closed, even if the operation is cancelled or fails.
 *
 * <p>Note that if you don't call {@code closeAsync()}, the captured objects will not be closed. The
 * automatic-closing approach described above is safer.
 *
 * @param <V> the type of the value of this step
 * @since 30.0
 */
// TODO(dpb): Consider reusing one CloseableList for the entire pipeline, modulo combinations.

* In `encoders.jsonable_encoder` remove parameter `sqlalchemy_safe`.
    * It was an early hack to allow returning SQLAlchemy models, but it was never documented, and the recommended way is using Pydantic's `orm_mode` as described in the tutorial: [SQL (Relational) Databases](https://fastapi.tiangolo.com/tutorial/sql-databases/).
    * PR [#1864](https://github.com/tiangolo/fastapi/pull/1864).

### Docs

b.limbs[:n] switch n { default: // Attempt to use a stack-allocated backing array. T := make([]uint, 0, preallocLimbs*2) if cap(T) < n*2 { T = make([]uint, 0, n*2) } T = T[:n*2] // This loop implements Word-by-Word Montgomery Multiplication, as // described in Algorithm 4 (Fig. 3) of "Efficient Software // Implementations of Modular Exponentiation" by Shay Gueron // [https://eprint.iacr.org/2011/239.pdf]. var c uint for i := 0; i < n; i++ { _ = T[n+i] // bounds check elimination hint // Step 1 (T =...