In our experience and testing, these types force us to represent data in inefficient ways because they are general purpose.
Ztunnel is not general purpose; it has an extremely tight goal.
We can exploit this to make a more efficient protocol, which is critical to achieve our resource footprint goals.

  pop_backward_tape.release()();

  std::vector<int64_t> targets;
  std::vector<Gradient*> used_in_grads;
  // We may end up with slightly fewer elements than we reserve, but grad.size()
  // should be a reasonably tight upper bound.
  targets.reserve(grad.size());
  used_in_grads.reserve(grad.size());
  std::unordered_map<int64_t, TapeTensor> sources_that_are_targets;

<component name="ProjectRunConfigurationManager">
  <configuration default="false" name="Symbol Light Classes Tests" type="GradleRunConfiguration" factoryName="Gradle" folderName="Analysis">
    <ExternalSystemSettings>
      <option name="executionName" />
      <option name="externalProjectPath" value="$PROJECT_DIR$" />
      <option name="externalSystemIdString" value="GRADLE" />
      <option name="scriptParameters" value="" />
      <option name="taskDescriptions">

### Suggest solutions

* After being able to understand the question, you can give them a possible **answer**.

* In many cases, it's better to understand their **underlying problem or use case**, because there might be a better way to solve it than what they are trying to do.

    int left = 0;
    int right = 2 * k - 1;

    int minThresholdPosition = 0;
    // The leftmost position at which the greatest of the k lower elements
    // -- the new value of threshold -- might be found.

    int iterations = 0;
    int maxIterations = IntMath.log2(right - left, RoundingMode.CEILING) * 3;
    while (left < right) {
      int pivotIndex = (left + right + 1) >>> 1;

---

If you just don't know, use normal `def`.

---

**Note**: You can mix `def` and `async def` in your *path operation functions* as much as you need and define each one using the best option for you. FastAPI will do the right thing with them.

Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast.

But by following the steps above, it will be able to do some performance optimizations.

And then, right after the `yield`, we unload the model. This code will be executed **after** the application **finishes handling requests**, right before the *shutdown*. This could, for example, release resources like memory or a GPU.

!!! tip

        rnd.nextBytes(left);
        byte[] right = left.clone();
        assertThat(comparator.compare(left, right)).isEqualTo(0);
        int i = rnd.nextInt(left.length);
        left[i] ^= (byte) (1 + rnd.nextInt(255));
        assertThat(comparator.compare(left, right)).isNotEqualTo(0);
        assertThat(UnsignedBytes.compare(left[i], right[i]) > 0)
            .isEqualTo(comparator.compare(left, right) > 0);
      }
    }
  }

     *
     * ```kt
     * fun usage(action: String.(Int) -> String) {
     *   "hello".<expr>action</expr>(10)
     * }
     * ```
     *
     * The user might want to know the type of the `action` callback. If we always return null for the named references,
     * we won't be able to handle this request, and just return null. So the user will only be able to see the type

    @Override
    public int compare(@CheckForNull Object left, @CheckForNull Object right) {
      if (left == right) {
        return 0;
      } else if (left == null) {
        return -1;
      } else if (right == null) {
        return 1;
      }
      int leftCode = identityHashCode(left);
      int rightCode = identityHashCode(right);
      if (leftCode != rightCode) {
        return leftCode < rightCode ? -1 : 1;