}
	weights[count] = uint8(highBit + 1)
	count++
	weightMark[highBit+1]++

	if weightMark[1] < 2 || weightMark[1]&1 != 0 {
		return 0, 0, r.makeError(off, "bad Huffman weights")
	}

	// Change weightMark from a count of weights to the index of
	// the first symbol for that weight. We shift the indexes to
	// also store how many we have seen so far,
	next := uint32(0)

                                        "type": "object",
                                        "additionalProperties": {"type": "number"},
                                    }
                                }
                            },
                        }
                    },
                    "summary": "Read Keyword Weights",

                                    "schema": {
                                        "title": "Response Read Keyword Weights Keyword Weights  Get",
                                        "type": "object",
                                        "additionalProperties": {"type": "number"},
                                    }
                                }
                            },
                        }

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

    // Apply float16 quantization to all the weights. Quantized weights will be
    // dequantized before running inference.
    // Activation: f32, Weight: f16, Bias: f16
    FLOAT16 = 3;

    // Apply static range quantization. The quantization range is determined
    // via calibration phase and quantized during conversion.
    // Activation: qi8, Weight: qi8, Bias: qi32
    POST_TRAINING_QUANTIZATION_STATIC_RANGE = 4;
  }

}

// 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,

// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions-drq="min-num-elements-for-weights=2500000" | FileCheck %s

// CHECK-LABEL: lift_float_matmul
func.func @lift_float_matmul(%arg0: tensor<1x12x12x512xf32>) -> (tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<512x512xf32>} : () -> tensor<512x512xf32>
  %out_1 = "tf.MatMul"(%arg0, %cst) {
    device = "", transpose_a = false, transpose_b = false