}
	state := &preScoreState{
		preferredNodeAffinity: preferredNodeAffinity,
	}
	cycleState.Write(preScoreStateKey, state)
	return nil
}

// Score returns the sum of the weights of the terms that match the Node.
// Terms came from the Pod .spec.affinity.nodeAffinity and from the plugin's
// default affinity.

// resource ops are considered duplicated if they have the same `shared_name`.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateMergeDuplicateResourceOpsPass();

// Apply quantization to weights based on the provided schemes.
std::unique_ptr<OperationPass<ModuleOp>> CreateQuantizeWeightsPass(
    const tensorflow::quantization::QuantizationOptions& quant_options);

// Propagate quantized type through allowed ops.

//       value {dimension_specs {dimension: 3}}}
//   }}
// }
// ```
//
// This preset:
//   * Applies per-channel quantization for weights (input index 1) of
//     convolution quantizable unit family. The quantization dimension is 3, the
//     channel dimension, which assumes the weight tensor is in NHWC format.
//   * Applies static-range PTQ for all other ops.
//
// Next ID: 4
message StaticRangePtqPreset {

constexpr mlir::StringRef kUnidirectionalSequenceRnnOp =
    "name: 'UnidirectionalSequenceRnn' input_arg: {name: 'Input' type: "
    "DT_FLOAT} input_arg: { name: 'Weights' type: DT_FLOAT } "
    "input_arg: { name: 'RecurrentWeights' type: DT_FLOAT } input_arg: { "
    "name: 'Bias' type: DT_FLOAT} "
    "input_arg: { name: 'HiddenState' type: DT_FLOAT} "
    "output_arg: { name: "

  // prepare_quantize_ptq_per_channel.mlir.
  Option<bool> enable_per_channel_quantization_{
      *this, "enable-per-channel-quantization", llvm::cl::init(false),
      llvm::cl::desc("Whether enable per-channel quantized weights.")};
};

bool PrepareQuantizePass::SetInputNodesQuantizationParams(func::FuncOp func) {
  StringRef func_name = func.getName();
  auto has_quantize_op = [&](const Value arg) {
    return (arg.hasOneUse() &&

		ret += edge.Weight
	}
	return ret
}

type edgeList []*Edge

func (el edgeList) Len() int {
	return len(el)
}

func (el edgeList) Less(i, j int) bool {
	if el[i].Weight != el[j].Weight {
		return abs64(el[i].Weight) > abs64(el[j].Weight)
	}

	from1 := el[i].Src.Info.PrintableName()
	from2 := el[j].Src.Info.PrintableName()
	if from1 != from2 {

					{Address: "20.0.0.1", Weight: 6},
					{Address: "20.0.0.2", Weight: 6},
					{Address: "20.0.0.3", Weight: 6},
					// 2 endpoint on network1 with weight aggregated at the gateway
					{Address: "1.1.1.1", Weight: 12},
					// 1 endpoint on network4 with no gateway (i.e. directly accessible)
					{Address: "40.0.0.1", Weight: 6},
				},
				Weight: 36,
			},
		},
		wantWorkloadMetadata: []string{

# Mapping of signature def key -> SignatureDef.
_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]

# Default minimum number of elements in the weights for them to be quantized
# during dynamic range quantization (DRQ) and weight-only quantization.
_DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS = 1024


def _is_qat_saved_model(saved_model_path: str):

  // broadcasting support. This needs to be run immediately after HLO->TF
  // legalization; otherwise other passes like `ConvertTFBroadcastTo` will
  // constant fold the newly generated TF broadcast ops and materialize the
  // weights.
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TF::CreateBroadcastFoldPass());

  // Canonicalization after TF legalization.
  pass_manager.addNestedPass<mlir::func::FuncOp>(

      // but work beyond that must take at least one second
      assertTrue(afterBurst >= 1000);
    }
  }

  /**
   * This neat test shows that no matter what weights we use in our requests, if we push X amount of
   * permits in a cool state, where X = rate * timeToCoolDown, and we have specified a
   * timeToWarmUp() period, it will cost as the prescribed amount of time. E.g., calling