* tailored to that use case.
 *
 * <p>This uses the same efficient implementation as {@link Ordering#leastOf(Iterable, int)},
 * offering expected O(n + k log k) performance (worst case O(n log k)) for n calls to {@link
 * #offer} and a call to {@link #topK}, with O(k) memory. In comparison, quickselect has the same

import com.google.common.base.MoreObjects;
import com.google.common.base.Objects;
import java.util.concurrent.Callable;
import javax.annotation.CheckForNull;

/**
 * Statistics about the performance of a {@link Cache}. Instances of this class are immutable.
 *
 * <p>Cache statistics are incremented according to the following rules:
 *
 * <ul>

import com.google.common.base.MoreObjects;
import com.google.common.base.Objects;
import java.util.concurrent.Callable;
import javax.annotation.CheckForNull;

/**
 * Statistics about the performance of a {@link Cache}. Instances of this class are immutable.
 *
 * <p>Cache statistics are incremented according to the following rules:
 *
 * <ul>

   * 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
   * them until they are needed.  When there is no contention, all

///

## Use `ORJSONResponse`

For example, if you are squeezing performance, you can install and use <a href="https://github.com/ijl/orjson" class="external-link" target="_blank">`orjson`</a> and set the response to be `ORJSONResponse`.

* Improve scheduler performance of MatchInterPodAffinity predicate. ([#57476](https://github.com/kubernetes/kubernetes/pull/57476), [@misterikkit](https://github.com/misterikkit))
* Improve scheduler performance of MatchInterPodAffinity predicate. ([#57477](https://github.com/kubernetes/kubernetes/pull/57477), [@misterikkit](https://github.com/misterikkit))

  AbstractContext* ctx_;
};

// Returns the number of elements in the gradient tensor.
int64_t TapeVSpace::NumElements(AbstractTensorHandle* tensor) const {
  // TODO(srbs): It seems like this is used only for performance optimization
  // and not for correctness. The only downside of keeping this 1 seems to be
  // that the gradient accumulation is unbounded and we will never
  // aggressively aggregate accumulated gradients to recover memory.

 * as the traditional heap data structure used in {@link PriorityQueue}.
 *
 * <p>This class is not thread-safe, and does not accept null elements.
 *
 * <p><i>Performance notes:</i>
 *
 * <ul>
 *   <li>If you only access one end of the queue, and do use a maximum size, this class will perform
 *       significantly worse than a {@code PriorityQueue} with manual eviction above the maximum

#include "tensorflow/c/c_api_macros.h"
#include "tensorflow/c/eager/c_api.h"

#ifdef __cplusplus
extern "C" {
#endif

// Resets `op_to_reset` with `op_or_function_name` and `raw_device_name`. This
// is for performance optimization by reusing an exiting unused op rather than
// creating a new op every time. If `raw_device_name` is `NULL` or empty, it
// does not set the device name. If it's not `NULL`, then it attempts to parse

 * href="https://www.javadoc.io/doc/com.github.ben-manes.caffeine/caffeine/latest/com.github.benmanes.caffeine/com/github/benmanes/caffeine/cache/Caffeine.html">{@code
 * Caffeine}</a> class. Caffeine offers better performance, more features (including asynchronous
 * loading), and fewer <a
 * href="https://github.com/google/guava/issues?q=is%3Aopen+is%3Aissue+label%3Apackage%3Dcache+label%3Atype%3Ddefect">bugs</a>.
 *