EnumSet<RdmaAccess> access = EnumSet.of(RdmaAccess.LOCAL_READ, RdmaAccess.LOCAL_WRITE);

        RdmaMemoryRegion region = provider.registerMemory(buffer, access);
        assertNotNull(region, "Memory region should not be null");
        assertEquals(buffer, region.getBuffer(), "Buffer should match");
        assertEquals(1024, region.getSize(), "Size should match buffer size");

    false

-   type: input id: Cuda attributes: label: CUDA/cuDNN version description:
    placeholder: validations: required: false

-   type: input id: Gpu attributes: label: GPU model and memory description: if
    compiling from source placeholder: validations: required: false

-   type: textarea id: what-happened attributes: label: Current Behaviour?

	notificationEventsSkippedTotalMD    = NewCounterMD(notificationEventsSkippedTotal, "Events that were skipped to be sent to the targets due to the in-memory queue being full")
)

// loadClusterNotificationMetrics - `MetricsLoaderFn` for cluster notification metrics.
func loadClusterNotificationMetrics(_ context.Context, m MetricValues, _ *metricsCache) error {

   * {@link Reader} classes and Java versions, but both methods read the entire input into memory
   * immediately, so we discourage use of both when memory usage is a concern.
   *
   * @param r the object to read from
   * @return a mutable {@link List} containing all the lines
   * @throws IOException if an I/O error occurs
   */

   *
   * {@snippet :
   * Stream.of("foo", "quux", "banana", "elephant").collect(least(2, comparingInt(String::length)))
   * // returns {"foo", "quux"}
   * }
   *
   * <p>This {@code Collector} uses O(k) memory and takes expected time O(n) (worst-case O(n log
   * k)), as opposed to e.g. {@code Stream.sorted(comparator).limit(k)}, which currently takes O(n
   * log n) time and O(n) space.
   *

        /** File system errors (not found, access denied, etc.) */
        FILE_SYSTEM,
        /** Protocol errors (invalid message, unsupported operation, etc.) */
        PROTOCOL,
        /** Resource errors (out of memory, disk space, etc.) */
        RESOURCE,
        /** Configuration errors */
        CONFIGURATION,
        /** Transient errors that may succeed on retry */
        TRANSIENT,
        /** Unknown or unclassified errors */

models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

También podrías usar `from starlette.templating import Jinja2Templates`.

**FastAPI** proporciona el mismo `starlette.templating` como `fastapi.templating`, solo como una conveniencia para ti, el desarrollador. Pero la mayoría de los responses disponibles vienen directamente de Starlette. Lo mismo con `Request` y `StaticFiles`.

///

## Escribiendo plantillas { #writing-templates }

### Entiende la pregunta { #understand-the-question }

* Revisa si puedes entender cuál es el **propósito** y el caso de uso de la persona que pregunta.

* Luego revisa si la pregunta (la gran mayoría son preguntas) es **clara**.

# ADR-0002 - Avoid using Java serialization

## Status

- ACCEPTED on 2023-12-01

## Context

In Gradle we often need to serialize in-memory objects for caching, or to transmit them across process barriers, etc.
Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.