machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application...

/**
 * This class provides a skeletal implementation of the {@link SortedMultiset} interface.
 *
 * <p>The {@link #count} and {@link #size} implementations all iterate across the set returned by
 * {@link Multiset#entrySet()}, as do many methods acting on the set returned by {@link
 * #elementSet()}. Override those methods for better performance.
 *
 * @author Louis Wasserman
 */
@GwtCompatible

# ADR-0001 - Use Architectural Decision Records

## Date

2023-12-01

## Context

In a distributed team with many subteams, the best solution to communicate decisions is to use a format accessible by everyone in charge of development.

We use *Specification* and *Discovery* documents stored in Google Drive, but they present some downsides:

    inadvertently changed this behavior when we introduced the `mockwebserver3` API.


## Version 5.0.0-alpha.17

_2025-06-29_

This release stabilizes many APIs for the imminent OkHttp 5.0.0 release.

 *  New: `TrailersSource`, a public API for HTTP trailers. Production callers shouldn't need this

    /*
     * The entrySet() of ConcurrentHashMap, unlike that of other Map
     * implementations, supports add() under JDK8. This seems problematic, but I
     * didn't see that discussed in the review, which included many other
     * changes: https://mail.openjdk.org/pipermail/core-libs-dev/2013-May/thread.html#17367
     *
     * TODO(cpovirk): decide what the best long-term action here is: force users

This is very useful when you need to:

* Have shared logic (the same code logic again and again).
* Share database connections.
* Enforce security, authentication, role requirements, etc.
* And many other things...

All these, while minimizing code repetition.

## First Steps { #first-steps }

Let's see a very simple example. It will be so simple that it is not very useful, for now.

   * {@code Set}.
   *
   * <p>The function is applied lazily, invoked when needed. This is necessary for the returned
   * multimap to be a view, but it means that the function will be applied many times for bulk
   * operations like {@link Multimap#containsValue} and {@code Multimap.toString()}. For this to
   * perform well, {@code function} should be fast. To avoid lazy evaluation when the returned

 */

package com.google.common.math;

import java.math.BigInteger;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying

    }

    @Test
    @DisplayName("Should handle maximum buffer size")
    void testMaximumBufferSize() throws Exception {
        // Given - large buffer with many notifications
        byte[] buffer = new byte[65536];
        int offset = 0;

        setHeaderStart(response, 64);

        // Calculate space for notifications

     * <li>Cleans up processed data and manages crawler sessions</li>
     * </ul>
     *
     * <p>The method includes error handling, retry logic, and will terminate
     * if too many empty results are encountered or if a system shutdown is requested.
     */
    @Override
    public void run() {
        if (dataService == null) {