def test_post_body(client: TestClient):
    data = {"2": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


def test_post_invalid_body(client: TestClient):
    data = {"foo": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 422, response.text
    assert response.json() == IsDict(

    data = {"2": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


@needs_py39
def test_post_invalid_body(client: TestClient):
    data = {"foo": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 422, response.text

}

// NamedEdgeMap contains all unique call edges in the profile and their
// edge weight.
type NamedEdgeMap struct {
	Weight map[NamedCallEdge]int64

	// ByWeight lists all keys in Weight, sorted by edge weight from
	// highest to lowest.
	ByWeight []NamedCallEdge
}

func emptyProfile() *Profile {
	// Initialize empty maps/slices for easier use without a requiring a

      "composite_conv.*");

  // Enable per-channel quantization for convolution weights.
  QuantizedType conv_weight_quantized_type{};

  // Assumes NHWC format, specifying the channel dimension (3) as the
  // quantized axis.
  conv_weight_quantized_type.mutable_dimension_specs()->set_dimension(3);

  // The index of weight operands passed to lifted functions for convolution
  // is 1.

// storage type. The available values use the full range of the storage value,
// i.e. [-128, 127]. Assumes asymmetric quantization, meaning the zero point
// value can be a non-zero value.
// If `narrow_range` is set true (ex: for weights), a restricted range of
// integers will be used for symmetric mapping, i.e. [-127, 127].
UniformQuantizedType CreateI8F32UniformQuantizedType(Location loc,
                                                     MLIRContext& context,

// point constant.
def : Pat<(TFL_DequantizeOp
             (TFL_QuantizeOp (Arith_ConstantOp F32ElementsAttr:$cst), $qt)),
          (TFL_ConstOp $cst)>;

// Transpose conv supports hybrid computation with quantized weights.
def FoldQuantWeightsIntoTposeConv : Pat<
  (TFL_TransposeConvOp
    $output_shape,
    (TFL_DequantizeOp $quant_weights),
    $quant_input,
    $bias, $padding, $stride_h, $stride_w, $faf),

		return scaleFactor
	}
	weight := uint32(math.MaxUint32)
	if ep.GetLoadBalancingWeight().Value < math.MaxUint32/scaleFactor {
		weight = ep.GetLoadBalancingWeight().Value * scaleFactor
	}
	return weight
}

// Apply the weight for this endpoint to the network gateways.

	}
	return postProcessNamedEdgeMap(weight, totalWeight)
}

func sortByWeight(edges []NamedCallEdge, weight map[NamedCallEdge]int64) {
	sort.Slice(edges, func(i, j int) bool {
		ei, ej := edges[i], edges[j]
		if wi, wj := weight[ei], weight[ej]; wi != wj {
			return wi > wj // want larger weight first
		}
		// same weight, order by name/line number
		if ei.CallerName != ej.CallerName {

    // determined. The activation and weight are quantized to INT8 while bias is
    // quantized to INT32.
    METHOD_STATIC_RANGE_INT8 = 2;

    // Dynamic range quantization. Quantized tensor values' ranges are
    // determined in the graph executions. The weights are quantized during
    // conversion.
    METHOD_DYNAMIC_RANGE_INT8 = 3;

    // Weight-only quantization. Only weights are quantized during conversion.

      enable_per_channel_quantized_weight_;
  // Change this to user-given bit width once we have custom configuration.
  options.bit_width_ = 8;

  // Insert quantization parameters for weights for ops with `weight_only_ptq`
  // attribute.
  pm.addNestedPass<func::FuncOp>(createInsertWeightParamPass());

  // PrepareQuantizePass uses SymbolTable to fetch relevant GEMM ops for