* perform better than its longhand equivalent, but should not perform worse.
   *
   * @since 12.0
   */
  HashCode hashInt(int input);

  /**
   * Shortcut for {@code newHasher().putLong(input).hash()}; returns the hash code for the given
   * {@code long} value, interpreted in little-endian byte order. The implementation <i>might</i>

   * perform better than its longhand equivalent, but should not perform worse.
   *
   * @since 12.0
   */
  HashCode hashInt(int input);

  /**
   * Shortcut for {@code newHasher().putLong(input).hash()}; returns the hash code for the given
   * {@code long} value, interpreted in little-endian byte order. The implementation <i>might</i>

### Modern Java 21 Support
- **Pattern Matching** - Leverages modern Java pattern matching for efficient type checking and conversion
- **Switch Expressions** - Optimized implementations using switch expressions for better performance
- **Sequenced Collections** - Full support for Java 21 sequenced collections API with dedicated utility methods
- **Performance Focused** - Optimized implementations for better runtime performance with reduced memory allocation

We don't really need to inherit from `HeroBase` because we are re-declaring all the fields. I'll leave it inheriting just for consistency, but this is not necessary. It's more a matter of personal taste. 🤷

The fields of `HeroUpdate` are:

* `name`
* `age`
* `secret_name`

{* ../../docs_src/sql_databases/tutorial002_an_py310.py ln[7:28] hl[25:28] *}

   * Each bit is set to 1 for all remainders that indicate divisibility by 2, 3, or 5, so
   * 1, 7, 11, 13, 17, 19, 23, 29 are set to 0. 30 and up don't matter because they won't be hit.
   */
  private static final int SIEVE_30 =
      ~((1 << 1) | (1 << 7) | (1 << 11) | (1 << 13) | (1 << 17) | (1 << 19) | (1 << 23)
          | (1 << 29));

  /**

  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

  // Backing memory for TensorId fields in opts.
  // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this.
  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {
  std::vector<TF_Output> return_tensors;
  std::vector<TF_Operation*> return_nodes;

* Then check if the question (the vast majority are questions) is **clear**.

* In many cases the question asked is about an imaginary solution from the user, but there might be a **better** one. If you can understand the problem and use case better, you might be able to suggest a better **alternative solution**.

* If you can't understand the question, ask for more **details**.

### Reproduce the problem { #reproduce-the-problem }

the same address may also share the same underlying TCP socket connection. Sharing a connection has substantial performance benefits: lower latency, higher throughput (due to [TCP slow start](https://www.igvita.com/2011/10/20/faster-web-vs-tcp-slow-start/)) and conserved battery. OkHttp uses a [ConnectionPool](https://square.github.io/okhttp/4.x/okhttp/okhttp3/-connection-pool/) that automatically reuses HTTP/1.x connections and multiplexes HTTP/2 connections.

In OkHttp some fields of the address...

    * A "token" is just a string with some content that we can use later to verify this user.
    * Normally, a token is set to expire after some time.
        * So, the user will have to log in again at some point later.
        * And if the token is stolen, the risk is less. It is not like a permanent key that will work forever (in most of the cases).

1. This is the generator function. It's a "generator function" because it contains `yield` statements inside.
2. By using a `with` block, we make sure that the file-like object is closed after the generator function is done. So, after it finishes sending the response.