// Changes before the partition is retrieved are reflected
    list.set(0, 3);

    Iterator<List<Integer>> iterator = partitions.iterator();

    // Changes before the partition is retrieved are reflected
    list.set(1, 4);

    List<Integer> first = iterator.next();

    // Changes after are not
    list.set(0, 5);

			Key:   config.Enable,
			Value: config.EnableOn,
		},
		config.KV{
			Key:   target.AmqpURL,
			Value: cfg.URL.String(),
		},
		config.KV{
			Key:   target.AmqpExchange,
			Value: cfg.Exchange,
		},
		config.KV{
			Key:   target.AmqpRoutingKey,
			Value: cfg.RoutingKey,
		},
		config.KV{
			Key:   target.AmqpExchangeType,
			Value: cfg.ExchangeType,
		},
		config.KV{

                            layout.buildDirectory.dir("empty-dir").get(),
                            rootProject.files("$sourceRoots"),
                            "2.0",
                            file("test-api-changes.json"),
                            rootProject.layout.projectDirectory,
                            newUpgradedPropertiesFile.get().asFile,
                            oldUpgradedPropertiesFile.get().asFile,

    List<Integer> list = asList(1, 2);
    Iterator<List<Integer>> partitions = Iterators.partition(list.iterator(), 1);

    // Changes before the partition is retrieved are reflected
    list.set(0, 3);
    List<Integer> first = partitions.next();

    // Changes after are not
    list.set(0, 4);

    assertEquals(ImmutableList.of(3), first);
  }

 * are inherently approximate, routinely affected by periodic clock corrections. Because this class
 * (by default) uses {@link System#nanoTime}, it is unaffected by these changes.
 *
 * <p>Use this class instead of direct calls to {@link System#nanoTime} for two reasons:
 *
 * <ul>
 *   <li>The raw {@code long} values returned by {@code nanoTime} are meaningless and unsafe to use

apply fixes to bugs or security vulnerabilities:

*   Clone the TensorFlow repository and switch to the appropriate branch for
    your desired version—for example, `r2.8` for version 2.8.
*   Apply the desired changes (i.e., cherry-pick them) and resolve any code
    conflicts.
*   Run TensorFlow tests and ensure they pass.
*   [Build](https://www.tensorflow.org/install/source) the TensorFlow pip
    package from source.

  private val writerQueue = taskRunner.newQueue()

  /** Ensures push promise callbacks events are sent in order per stream. */
  private val pushQueue = taskRunner.newQueue()

  /** Notifies the listener of settings changes. */
  private val settingsListenerQueue = taskRunner.newQueue()

  /** User code to run in response to push promise events. */
  private val pushObserver: PushObserver = builder.pushObserver

 * are inherently approximate, routinely affected by periodic clock corrections. Because this class
 * (by default) uses {@link System#nanoTime}, it is unaffected by these changes.
 *
 * <p>Use this class instead of direct calls to {@link System#nanoTime} for two reasons:
 *
 * <ul>
 *   <li>The raw {@code long} values returned by {@code nanoTime} are meaningless and unsafe to use

        // Assert
        assertArrayEquals(r2, r1);
        assertEquals(24, r1.length);
    }

    @Test
    @DisplayName("getNTLM2Response: returns 24 bytes and changes with inputs")
    void testGetNTLM2Response_basic() throws GeneralSecurityException {
        // Arrange
        byte[] pwdHash = NtlmUtil.getNTHash("password");
        byte[] server = hex("0102030405060708");

For the things that need changes in your Pydantic models, the Pydantic team built [`bump-pydantic`](https://github.com/pydantic/bump-pydantic).

A command line tool that will **process your code** and update most of the things **automatically** for you. Make sure you have your code in git first, and review each of the changes to make sure everything is correct before committing the changes.

### Pydantic v1