* {@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
   */

import com.google.errorprone.annotations.CanIgnoreReturnValue;
import java.io.DataInput;
import java.io.IOException;
import org.jspecify.annotations.Nullable;

/**
 * An extension of {@code DataInput} for reading from in-memory byte arrays; its methods offer
 * identical functionality but do not throw {@link IOException}.
 *
 * <p><b>Warning:</b> The caller is responsible for not attempting to read past the end of the

        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");

Each <code>DATA</code> directive initializes a section of the
corresponding memory.
The memory not explicitly initialized is zeroed.
The general form of the <code>DATA</code> directive is

<pre>
DATA	symbol+offset(SB)/width, value
</pre>

<p>
which initializes the symbol memory at the given offset and width with the given value.

 * <p>This class should not be assumed to be universally superior to {@code java.util.HashSet}.
 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Dimitris Andreou
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

    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?

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

# 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.

                String.format(Locale.ROOT, "Caught an exception in %s.  Shutting down.", t),
                e);
      } catch (Throwable errorInLogging) { // sneaky checked exception
        // If logging fails, e.g. due to missing memory, at least try to log the
        // message and the cause for the failed logging.
        System.err.println(e.getMessage());
        System.err.println(errorInLogging.getMessage());
      } finally {
        runtime.exit(1);

        searchEngineClient = new SearchEngineClient() {
            @Override
            public long deleteByQuery(String index, QueryBuilder query) {
                throw new OutOfMemoryError("Out of memory");
            }
        };
        ComponentUtil.register(searchEngineClient, "searchEngineClient");

        // OutOfMemoryError is an Error, not Exception, so it will not be caught