// This is a helper function which acquires mutexes in-order to provide
// thread-safe way of performing weights update during the optimizer op. It
// returns an opaque LockHolder handle back to plugin. This handle is passed to
// the Release API for releasing the locks when the weight update is done. The
// caller takes ownership of the `source` and `dest` tensors and is responsible
// for freeing them with TF_DeleteTensor.

!!! tip
    ✔️ 🤯 👈 🎻 🕴 🐕‍🦺 `str` 🔑.

    ✋️ Pydantic ✔️ 🏧 💽 🛠️.

    👉 ⛓ 👈, ✋️ 👆 🛠️ 👩‍💻 💪 🕴 📨 🎻 🔑, 📏 👈 🎻 🔌 😁 🔢, Pydantic 🔜 🗜 👫 & ✔ 👫.

     & `dict` 👆 📨 `weights` 🔜 🤙 ✔️ `int` 🔑 & `float` 💲.

## 🌃

⏮️ **FastAPI** 👆 ✔️ 🔆 💪 🚚 Pydantic 🏷, ⏪ 🚧 👆 📟 🙅, 📏 & 😍.

✋️ ⏮️ 🌐 💰:

* 👨‍🎨 🐕‍🦺 (🛠️ 🌐 ❗)
* 💽 🛠️ (.Ⓜ.. ✍ / 🛠️)
* 💽 🔬

    Isso significa que, embora os clientes da API só possam enviar strings como chaves, desde que essas strings contenham inteiros puros, o Pydantic irá convertê-los e validá-los.

    E o `dict` que você recebe como `weights` terá, na verdade, chaves `int` e valores` float`.

## Recapitulação

Com **FastAPI** você tem a flexibilidade máxima fornecida pelos modelos Pydantic, enquanto seu código é mantido simples, curto e elegante.

}

func (m scoreMap) processTerm(term *framework.AffinityTerm, weight int32, pod *v1.Pod, nsLabels labels.Set, node *v1.Node, multiplier int32) {
	if term.Matches(pod, nsLabels) {
		if tpValue, tpValueExist := node.Labels[term.TopologyKey]; tpValueExist {
			if m[term.TopologyKey] == nil {
				m[term.TopologyKey] = make(map[string]int64)
			}
			m[term.TopologyKey][tpValue] += int64(weight * multiplier)
		}
	}
}

inline constexpr std::array<int64_t, 4> kNchwToNhwcPermutation = {0, 2, 3, 1};

// Permutation from the OIHW (== (output features, input features, height,
// width)) tensor format to HWIO. This is commonly used to transpose convolution
// weights represented as OIHW format to HWIO, which is more desirable for
// certain downstream optimization passes (e.g. XLA).

// “[The least weight subsequence problem],” FOCS 1985, pp. 137-143.
//
// [The least weight subsequence problem]: https://doi.org/10.1109/SFCS.1985.60
func wrap(words []string, max int) (seq []int) {
	// The algorithm requires that our scoring function be concave,
	// meaning that for all i₀ ≤ i₁ < j₀ ≤ j₁,
	// weight(i₀, j₀) + weight(i₁, j₁) ≤ weight(i₀, j₁) + weight(i₁, j₀).
	//

                                    enable_per_channel_quantization_));

  // Apply activation-weight quantization.
  if (quantization_method_ ==
      tensorflow::quantization::QuantizationMethod::METHOD_STATIC_RANGE_INT8) {
    // For XLA case, weight quantization will be applied for the remaining f32
    // weights even in SRQ.
    pm.addNestedPass<func::FuncOp>(

  SPARSE = 1,
  DENSE = 2,
}

table LSHProjectionOptions {
  type: LSHProjectionType;
}

table SVDFOptions {
  rank:int;
  fused_activation_function:ActivationFunctionType;
  // For weights-only quantization, use asymmetric quantization for non
  // constant inputs at evaluation time.
  asymmetric_quantize_inputs:bool;
}

// An implementation of TensorFlow RNNCell.
table RNNOptions {

}

func (e *LocalityEndpoints) refreshWeight() {
	var weight *wrapperspb.UInt32Value
	if len(e.llbEndpoints.LbEndpoints) == 0 {
		weight = nil
	} else {
		weight = &wrapperspb.UInt32Value{}
		for _, lbEp := range e.llbEndpoints.LbEndpoints {
			weight.Value += lbEp.GetLoadBalancingWeight().Value
		}
	}
	e.llbEndpoints.LoadBalancingWeight = weight
}

func (e *LocalityEndpoints) AssertInvarianceInTest() {

              "Non-constant weights are not supported at the moment,"
              " except matmul and einsum.");
        } else if (!quant_options_.enable_two_input_tensors() &&
                   !is_unitwise_quantization_enabled) {
          return absl::InternalError(
              "Quantization is disabled for this op due to the non-constant "
              "weight. You can enable it by setting `enable_two_input_tensors` "