std::vector<ParallelTensor*> parallel_inputs;
  std::vector<std::unique_ptr<ParallelTensor>> implicitly_broadcast_tensors;
  parallel_inputs.reserve(inputs.size());
  implicitly_broadcast_tensors.reserve(inputs.size());  // not tight
  for (const auto& input : inputs) {
    if (absl::holds_alternative<TFE_TensorHandle*>(input)) {
      if (operation_name == std::string("_EagerConst")) {

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;

### 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;

   * 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>

---

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.

          return classify(Ints.compare(left, right));
        }

        @Override
        public ComparisonChain compare(long left, long right) {
          return classify(Longs.compare(left, right));
        }

        @Override
        public ComparisonChain compare(float left, float right) {
          return classify(Float.compare(left, right));
        }

        @Override

        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);
      }
    }
  }

<code>R0&lt;&lt;16</code>
<br>
<code>R0@&gt;16</code>:
For <code>&lt;&lt;</code>, left shift <code>R0</code> by 16 bits.
The other codes are <code>-&gt;</code> (arithmetic right shift),
<code>&gt;&gt;</code> (logical right shift), and
<code>@&gt;</code> (rotate right).
</li>

<li>
<code>R0-&gt;R1</code>
<br>
<code>R0&gt;&gt;R1</code>
<br>
<code>R0&lt;&lt;R1</code>
<br>
<code>R0@&gt;R1</code>: