// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="min-elements-for-weights=4000 enable-custom-op-quantization=CustomTestOp=1-3,CustomTestOp3=3" | FileCheck --check-prefix=MinElement %s
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="min-elements-for-weights=19" | FileCheck --check-prefix=LSTMOpQuantized %s
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range="min-elements-for-weights=21" | FileCheck --check-prefix=LSTMOpNotQuantized %s

```

!!! tip "팁"
    JSON은 오직 `str`형 키만 지원한다는 것을 염두에 두세요.

    하지만 Pydantic은 자동 데이터 변환이 있습니다.

    즉, API 클라이언트가 문자열을 키로 보내더라도 해당 문자열이 순수한 정수를 포함하는한 Pydantic은 이를 변환하고 검증합니다.

    그러므로 `weights`로 받은 `dict`는 실제로 `int` 키와 `float` 값을 가집니다.

## 요약

**FastAPI**를 사용하면 Pydantic 모델이 제공하는 최대 유연성을 확보하면서 코드를 간단하고 짧게, 그리고 우아하게 유지할 수 있습니다.

물론 아래의 이점도 있습니다:

* 편집기 지원 (자동완성이 어디서나!)
* 데이터 변환 (일명 파싱/직렬화)

    А `dict`, с именем `weights`, который вы получите в качестве ответа Pydantic, действительно будет иметь ключи типа `int` и значения типа `float`.

## Резюме

    This means that, even though your API clients can only send strings as keys, as long as those strings contain pure integers, Pydantic will convert them and validate them.

    And the `dict` you receive as `weights` will actually have `int` keys and `float` values.

## Recap

With **FastAPI** you have the maximum flexibility provided by Pydantic models, while keeping your code simple, short and elegant.

    Das bedeutet, dass Ihre API-Clients nur Strings senden können, aber solange diese Strings nur Zahlen enthalten, wird Pydantic sie konvertieren und validieren.

    Und das `dict` welches Sie als `weights` erhalten, wird `int`-Schlüssel und `float`-Werte haben.

## Zusammenfassung

Mit **FastAPI** haben Sie die maximale Flexibilität von Pydantic-Modellen, während Ihr Code einfach, kurz und elegant bleibt.

                    type.getNumElements());
  }

  return sparsity;
}

typedef struct InspectResult {
  // Whether the weight tensor is sparse enough to be compressed.
  bool can_compress;
  // If the weight tensor cannot be encoded in a block configuration that the op
  // supports, a Densify() op will be inserted afterwards to fall back to dense
  // execution.
  bool needs_densify;

  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 {

		// encountered, let the individual Score weight take precedence.
		if _, ok := f.scorePluginWeight[e.Name]; ok {
			continue
		}
		// a weight of zero is not permitted, plugins can be disabled explicitly
		// when configured.
		f.scorePluginWeight[e.Name] = int(e.Weight)
		if f.scorePluginWeight[e.Name] == 0 {
			f.scorePluginWeight[e.Name] = 1
		}

	}
	state := &preScoreState{
		preferredNodeAffinity: preferredNodeAffinity,
	}
	cycleState.Write(preScoreStateKey, state)
	return nil
}

// Score returns the sum of the weights of the terms that match the Node.
// Terms came from the Pod .spec.affinity.nodeAffinity and from the plugin's
// default affinity.

// resource ops are considered duplicated if they have the same `shared_name`.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateMergeDuplicateResourceOpsPass();

// Apply quantization to weights based on the provided schemes.
std::unique_ptr<OperationPass<ModuleOp>> CreateQuantizeWeightsPass(
    const tensorflow::quantization::QuantizationOptions& quant_options);

// Propagate quantized type through allowed ops.