}
	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)

      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights/read"
  op: "Identity"
  input: "BoxPredictor_4/ClassPredictor/weights"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@BoxPredictor_4/ClassPredictor/weights"
      }
    }
  }
}
node {

	// Type selects which strategy to run.
	Type ScoringStrategyType

	// Resources to consider when scoring.
	// The default resource set includes "cpu" and "memory" with an equal weight.
	// Allowed weights go from 1 to 100.
	// Weight defaults to 1 if not specified or explicitly set to 0.
	Resources []ResourceSpec

	// Arguments specific to RequestedToCapacityRatio strategy.

    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

      "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.

		},
		// Consider Affinity, Anti Affinity and symmetry together.
		// for Affinity, the weights are:                8,  0,  0,  0
		// for Anti Affinity, the weights are:           0, -5,  0,  0
		// for Affinity symmetry, the weights are:       0,  0,  8,  0
		// for Anti Affinity symmetry, the weights are:  0,  0,  0, -5
		{

		if len(f) < 6 {
			continue
		}
		port, _, portOk := dtoi(f[4])
		priority, _, priorityOk := dtoi(f[3])
		weight, _, weightOk := dtoi(f[2])
		if !(portOk && priorityOk && weightOk) {
			continue
		}
		addrs = append(addrs, &SRV{absDomainName(f[5]), uint16(port), uint16(priority), uint16(weight)})
		cname = absDomainName(f[0])
	}
	byPriorityWeight(addrs).sort()
	return
}

}

func (m scoreMap) processTerm(term *framework.AffinityTerm, weight int32, pod *v1.Pod, nsLabels labels.Set, node *v1.Node, multiplier int32) {
	if term.Matches(pod, nsLabels) {
		if tpValue, tpValueExist := node.Labels[term.TopologyKey]; tpValueExist {
			if m[term.TopologyKey] == nil {
				m[term.TopologyKey] = make(map[string]int64)
			}
			m[term.TopologyKey][tpValue] += int64(weight * multiplier)
		}
	}
}

    LOG(INFO) << quantized_tensor->name()->str() << " "
              << float_tensor->name()->str();
    if (ExpectEqualTensor(quantized_tensor, float_tensor)) {
      if (quantized && quantized_tensor->name()->str().find("weights")) {
        // If tensor is quantized, data type and buffer contents can be
        // different between float and quantized tensors. So do those tests
        // separately in the test body without checking them here.

  // For now, restrict scale adjustment to ops with affine quantized weights,
  // and having weights and biases as constants. This currently only applies to
  // FC and Conv* ops. Restriction for the weight can be relaxed if there are
  // needs for adjusting scale of variable weights.
  auto affine_op = dyn_cast<AffineQuantizedOpInterface>(op);