}
    storage_type = mlir::cast<mlir::IntegerType>(raw_elem_type);
  }

  // TFlite uses narrow-range [u]int8 for constant buffers of quantized weights.
  // Since we don't know which ones are weights, we represent this optimization
  // as a change in the storage bounds for the type for all constants of this
  // type.
  const int bitwidth = storage_type.getIntOrFloatBitWidth();

			continue
		}

		entry, weight := split(entry, ';')

		// Scan the language.
		t, err := Parse(entry)
		if err != nil {
			id, ok := acceptFallback[entry]
			if !ok {
				return nil, nil, err
			}
			t = makeTag(language.Tag{LangID: id})
		}

		// Scan the optional weight.
		w := 1.0
		if weight != "" {
			weight = consume(weight, 'q')
			weight = consume(weight, '=')

						Port:   &networking.PortSelector{Number: 443},
						Subset: "prod",
					},
					Weight: 75,
				},
				{
					Destination: &networking.Destination{
						Host:   "example-canary.com",
						Port:   &networking.PortSelector{Number: 443},
						Subset: "canary",
					},
					Weight: 25,
				},
			},
		},
	}

	for _, tt := range cases {
		t.Run(tt.name, func(t *testing.T) {

  OpSet op_set_;

  Option<bool> enable_per_channel_quantization_{
      *this, "enable-per-channel-quantization", llvm::cl::init(false),
      llvm::cl::desc("Whether enable per-channel quantized weights.")};
};

// If the weight is applicable to dynamic range quantization, insert Quantize
// and Dequantize ops with per-tensor scale.
class PrepareDRQQuantizableOp : public OpRewritePattern<arith::ConstantOp> {
 public:

  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 {

  // NoFusing-LABEL: FuseMulWithFullyConnectedNoBias
  // NoFusing-DAG: %[[MWEIGHTS:.*]] = arith.constant dense<2.000000e+00> : tensor<512xf32>
  // NoFusing-DAG: %[[WEIGHTS:.*]] = arith.constant dense<3.000000e+00> : tensor<1024x512xf32>
  // NoFusing-DAG: %[[BIAS:.*]] = "tfl.no_value"() <{value}> : () -> none

          &q_builder, input_model, quantized_type, use_updated_hybrid_scheme,
          ::tflite::optimize::QuantizerType::OLD_QUANTIZER) != kTfLiteOk) {
    return absl::InvalidArgumentError(
        "Quantize weights transformation failed.");
  }
  const uint8_t* q_buffer = q_builder.GetBufferPointer();
  *result =
      std::string(reinterpret_cast<const char*>(q_buffer), q_builder.GetSize());

  return absl::OkStatus();
}

// 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.