}

  /**
   * 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

    return stringWriter.toString();
  }

  /**
   * Returns the stack trace of {@code throwable}, possibly providing slower iteration over the full
   * trace but faster iteration over parts of the trace. Here, "slower" and "faster" are defined in
   * comparison to the normal way to access the stack trace, {@link Throwable#getStackTrace()

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:**

### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:

- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.

### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.

### 5. (optional) Any other info / logs

e","_longMonthsParse","_shortMonthsParse","firstDayOfYear","firstDayOfWeek","_weekdays","_weekdaysMin","_weekdaysShort","weekdayName","_weekdaysParseExact","_weekdaysParse","_shortWeekdaysParse","_minWeekdaysParse","_fullWeekdaysParse","charAt","isLower","langData","mathAbs","addSubtract$1","absCeil","daysToMonths","monthsToDays","makeAs","alias","as","asMilliseconds","asSeconds","asMinutes","asHours","asDays","asWeeks","asMonths","asQuarters","asYears","makeGetter","thresholds","relativeTime$1"...

* `compresslevel` - Used during GZip compression. It is an integer ranging from 1 to 9. Defaults to `9`. Lower value results in faster compression but larger file sizes, while higher value results in slower compression but smaller file sizes.

## Other middlewares

There are many other ASGI middlewares.

For example:

* **Starlette**:
    * Will have the next best performance, after Uvicorn. In fact, Starlette uses Uvicorn to run. So, it probably can only get "slower" than Uvicorn by having to execute more code.
    * But it provides you the tools to build simple web applications, with routing based on paths, etc.

   * because underutilization = free resources for the taking. If we are primarily interested to
   * deal with overflow, then stored permits could be given out /slower/ than fresh ones. Thus, we
   * require a (different in each case) function that translates storedPermits to throttling time.
   *
   * This role is played by storedPermitsToWaitTime(double storedPermits, double permitsToTake). The

				// when Writes() scale higher than Reads()
				// - practically speaking this never happens
				// and should never happen - however it has
				// been seen recently due to hardware issues
				// causes Reads() to go slower than Writes().
				//
				// Send such results anyways as this shall
				// expose a problem underneath.
				if totalPut > throughputHighestPut {
					throughputHighestResults = results

* Large payloads.

Only a single connection is ever made between two servers.
Likely this means that this connection will not be able to saturate network bandwidth.
Therefore, using this for large payloads will likely be slower than using a separate connection,
and other connections will be blocked while the large payload is being sent.

## Handlers & Routes

Handlers have a predefined Handler ID.