if a sidecar is present in the workload.
                      type: string
                    weight:
                      description: The load balancing weight associated with the endpoint.
                      maximum: 4294967295
                      minimum: 0
                      type: integer
                  type: object

      ValueReference<K, V> previous = entry.getValueReference();
      int weight = map.weigher.weigh(key, value);
      checkState(weight >= 0, "Weights must be non-negative");

      ValueReference<K, V> valueReference =
          map.valueStrength.referenceValue(this, entry, value, weight);
      entry.setValueReference(valueReference);
      recordWrite(entry, weight, now);
      previous.notifyNewValue(value);
    }

        enable_per_channel_quantized_weight_ &&
            IsWeightPerChannelQuantized(quantization_method));
  }

  // Returns true if the quantization method indicates per-channel quantization
  // for convolution weights. This method specifically matches a quantization
  // dimension of 3 for the input index 1 or unspecified quantization dimension
  // for the input index 1.

                            type: integer
                          weight:
                            default: 1
                            description: |-
                              Weight specifies the proportion of requests forwarded to the referenced
                              backend. This is computed as weight/(sum of all weights in this

        `tf.keras.mixed_precision.experimental.LossScaleOptimizer`, the weights
        of the `DynanmicLossScale` are copied into the `LossScaleOptimizer`
        instead of being reused. This means modifying the weights of the
        `DynamicLossScale` will no longer affect the weights of the
        LossScaleOptimizer, and vice versa.

            // input. Only int8 weight is supported for now.
            inputs.push_back(dq_op.getOperand());
            is_operand_or_result_modified = true;
          } else {
            // Otherwise, it's the case where the operand is activations or the
            // quantizing_op is non-supported/weight-only.
            inputs.push_back(operand);
          }
        } else {

}

// Returns the quantized type for the
// input_type/min/max/storag_type_width/narrow_range.
// This is entry point to the Quant dialect and used for both quantizing
// activations and weights.
Type GetQuantizedType(Builder builder, const Type input_type,
                      const ArrayRef<double> min, const ArrayRef<double> max,
                      const int quant_dim, const int storage_type_width,

format which contains three 1D tensors.
  }];

  let arguments = (ins
    TF_Int32Tensor:$indices_or_row_splits,
    TF_Int32Tensor:$values,
    TF_Float32Tensor:$weights,

    ConfinedAttr<I64Attr, [IntMinValue<1>]>:$sample_count,
    StrAttr:$combiner
  );

  let results = (outs
    TF_Int32Tensor:$row_ids,
    TF_Int32Tensor:$col_ids,

  }];

  let description = [{
Outputs a vector with length `size` and the same dtype as `weights`. If
`weights` are empty, then index `i` stores the number of times the value `i` is
counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
the value in `weights` at each index where the corresponding value in `arr` is
`i`.

Values in `arr` outside of the range [0, size) are ignored.

			Address: netutils.ParseIPSloppy(ip),
			Port:    uint16(portNum),
			Weight:  1,
		}

		if curEndpoints.Has(ep) {
			// if we are syncing for the first time, loop through all current destinations and
			// reset their weight.
			if proxier.initialSync {
				for _, dest := range curDests {
					if dest.Weight != newDest.Weight {
						err = proxier.ipvs.UpdateRealServer(appliedVirtualServer, newDest)