float_min = -float_max;
  }

  ASSERT_THAT(quantized_quant_params.scale, SizeIs(1));
  ASSERT_THAT(quantized_quant_params.zero_point, SizeIs(1));
  float scale = (float_max - float_min) / ((1 << bit_num) - 1);
  EXPECT_THAT(scale, FloatNear(quantized_quant_params.scale[0], eps));
}

class QuantizeModelTest : public testing::Test {
 protected:
  QuantizeModelTest() {

	if config.MaxPerCore != nil && *config.MaxPerCore > 0 {
		floor := 0
		if config.Min != nil {
			floor = int(*config.Min)
		}
		scaled := int(*config.MaxPerCore) * detectNumCPU()
		if scaled > floor {
			logger.V(3).Info("GetConntrackMax: using scaled conntrack-max-per-core")
			return scaled, nil
		}
		logger.V(3).Info("GetConntrackMax: using conntrack-min")
		return floor, nil
	}
	return 0, nil
}

// and NumericVerify ops to compare output values from the quantized and float
// ops.
//
// When `legacy_float_scale` is true, the quantizer will use float scale instead
// of double, and call TOCO's quantization routines to maintain bit-exactness of
// the values with the TOCO quantizer.
TfLiteStatus QuantizeModel(
    absl::string_view model_buffer, const tflite::TensorType &input_type,

    Arg<TF_NumberTensor, [{Must be a scalar.}]>:$beta1_power,
    Arg<TF_NumberTensor, [{Must be a scalar.}]>:$beta2_power,
    Arg<TF_NumberTensor, [{Scaling factor. Must be a scalar.}]>:$lr,
    Arg<TF_NumberTensor, [{Momentum factor. Must be a scalar.}]>:$beta1,
    Arg<TF_NumberTensor, [{Momentum factor. Must be a scalar.}]>:$beta2,
    Arg<TF_NumberTensor, [{Ridge term. Must be a scalar.}]>:$epsilon,

        auto shape = GetAsVector(quant_tensor->shape());
        if (kUseUpdatedHybridSchemeDefault) {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), shape[0]);
        } else {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), 1);
        }
      } else {
        EXPECT_EQ(quant_tensor->type(), TensorType_FLOAT32);
      }
    }

// It is used only for fractions, so does not return an error on overflow,
// it just stops accumulating precision.
func leadingFraction(s string) (x uint64, scale float64, rem string) {
	i := 0
	scale = 1
	overflow := false
	for ; i < len(s); i++ {
		c := s[i]
		if c < '0' || c > '9' {
			break
		}
		if overflow {
			continue
		}
		if x > (1<<63-1)/10 {

    auto scaled = rewriter.create<chlo::BroadcastMulOp>(
        op.getLoc(), result_type, iota, op.getDelta(),
        hlo::getBroadcastDimensionsAttr(&rewriter, iota, op.getDelta()));
    rewriter.replaceOpWithNewOp<chlo::BroadcastAddOp>(
        op, result_type, scaled, op.getStart(),
        hlo::getBroadcastDimensionsAttr(&rewriter, scaled, op.getStart()));
    return success();
  }
};

Articles: English: - author: Stephen Siegert - Neon link: https://neon.tech/blog/deploy-a-serverless-fastapi-app-with-neon-postgres-and-aws-app-runner-at-any-scale title: Deploy a Serverless FastAPI App with Neon Postgres and AWS App Runner at any scale - author: Kurtis Pykes - NVIDIA link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/ title: Building a Machine Learning Microservice with FastAPI - author: Ravgeet Dhillon - Twilio link: https://www.tw...

          mlir::dyn_cast<mlir::quant::UniformQuantizedType>(element_type)) {
    std::vector<float> scales = {static_cast<float>(qtype.getScale())};
    std::vector<int64_t> zero_points = {qtype.getZeroPoint()};
    q_params = tflite::CreateQuantizationParameters(
        builder_, /*min=*/0, /*max=*/0, builder_.CreateVector<float>(scales),
        builder_.CreateVector<int64_t>(zero_points));

			enableInPlacePodVerticalScaling: true,
			expectedHint:                    framework.QueueSkip,
		},
		"skip-queue-on-other-pod-resource-scale-up": {
			pod:                             st.MakePod().Name("pod1").Req(map[v1.ResourceName]string{v1.ResourceCPU: "1"}).Obj(),