include:
          - os: macos-latest
            python-version: "3.10"
            coverage: coverage
            uv-resolution: lowest-direct
          - os: windows-latest
            python-version: "3.12"
            coverage: coverage
            uv-resolution: lowest-direct
          - os: ubuntu-latest
            python-version: "3.13"
            coverage: coverage
            uv-resolution: highest

    // all values are lower or higher than centerFloor, in which case we find the two highest or
    // lowest respectively). If centerFloor is in allRequired, we will definitely find it. If not,
    // but centerFloor + 1 is, we'll definitely find that. The closest value to the true (unrounded)
    // center will be at either low or high.
    int low = requiredFrom;
    int high = requiredTo;

            }

            updateGroupBadge(checkbox.closest('.dropdown'));
            updateFilterBadge();
        });

        // Filter search input handler
        $(document).on('input', '.filter-search-input', function() {
            var query = $(this).val().toLowerCase();
            $(this).closest('.filter-dropdown-menu').find('.filter-check-item').each(function() {

      }
    }, BUTTON_DISABLE_DURATION);
    return true;
  });

  $(document).on("click touchend", function(e) {
    if (!$(e.target).closest("#searchOptions, #searchOptionsButton").length) {
      $("#searchOptions").removeClass("active");
    }
  });

  $("[data-toggle='control-options']").on("click", function(e) {
    e.preventDefault();

    assertInterrupted();
  }

  public void testAwaitTimeoutMultiInterruptExpired() {
    /*
     * We don't "need" to schedule a countDown() call at all here, but by doing
     * so, we come the closest we can to testing that the wait time is
     * appropriately decreased on each progressive await() call.
     */
    TimedLatch latch = new TimedLatch(/* countdownInMillis= */ LONG_DELAY_MS);

      private val source: BufferedSource = source.buffer()

      // Visible for testing.
      @JvmField var dynamicTable = arrayOfNulls<Header>(8)

      // Array is populated back to front, so new entries always have lowest index.
      private var nextHeaderIndex = dynamicTable.size - 1

      @JvmField var headerCount = 0

      @JvmField var dynamicTableByteCount = 0

      fun getAndResetHeaderList(): List<Header> {

  }

  /**
   * Similar to load64, but allows offset + 8 > input.length, padding the result with zeroes. This
   * has to explicitly reverse the order of the bytes as it packs them into the result which makes
   * it slower than the native version.
   *
   * @param input the input bytes
   * @param offset the offset into the array at which to start reading
   * @param length the number of bytes from the input to read

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.
This is due to Java's use of reflection and the need to maintain a lot of metadata.

- **Size of Serialized Data:**

        return [b[0] for b in files]

    original_read = StarletteUploadFile.read

    async def patched_read(self: StarletteUploadFile, size: int = -1) -> bytes:
        # Make the FIRST file slower *deterministically*
        if self.filename == "slow.txt":
            await anyio.sleep(0.05)
        return await original_read(self, size)

    monkeypatch.setattr(StarletteUploadFile, "read", patched_read)

## GraphQL with Strawberry { #graphql-with-strawberry }

If you need or want to work with **GraphQL**, [**Strawberry**](https://strawberry.rocks/) is the **recommended** library as it has the design closest to **FastAPI's** design, it's all based on **type annotations**.

Depending on your use case, you might prefer to use a different library, but if you asked me, I would probably suggest you try **Strawberry**.