}
	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)

	}

	lbEndpoints := eps[0].LbEndpoints
	if len(lbEndpoints) != len(expected.weights) {
		t.Fatalf("unexpected number of endpoints.\nWant:\n%v\nGot:\n%v", expected.getAddrs(), getLbEndpointAddrs(lbEndpoints))
	}

	for addr, weight := range expected.weights {
		var match *endpoint.LbEndpoint
		for _, ep := range lbEndpoints {

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

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

from fastapi import FastAPI

app = FastAPI()


@app.post("/index-weights/")
async def create_index_weights(weights: dict[int, float]):

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

    # output shape: [-1, 1024]
    fc1 = gen_mnist_ops.new_fully_connected(reshape, self.weights['f3'],
                                            self.biases['b3'], 'RELU')
    # output shape: [-1, 10]
    return gen_mnist_ops.new_fully_connected(fc1, self.weights['f4'],
                                             self.biases['b4'])


def main(strategy):