input.getDefiningOp())) {
        // Tensors with derived scale are biases, and handled in propagation.
        if (tensor_property.use_derived_scale) continue;
        // For weights, use quantization scale inferred from the values.
        if (failed(processConstantOp(op, input.getDefiningOp(), index,
                                     tensor_property, rewriter))) {
          return failure();

		},
		// 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
		{

    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.

    }
    storage_type = mlir::cast<mlir::IntegerType>(raw_elem_type);
  }

  // TFlite uses narrow-range [u]int8 for constant buffers of quantized weights.
  // Since we don't know which ones are weights, we represent this optimization
  // as a change in the storage bounds for the type for all constants of this
  // type.
  const int bitwidth = storage_type.getIntOrFloatBitWidth();

						Port:   &networking.PortSelector{Number: 443},
						Subset: "prod",
					},
					Weight: 75,
				},
				{
					Destination: &networking.Destination{
						Host:   "example-canary.com",
						Port:   &networking.PortSelector{Number: 443},
						Subset: "canary",
					},
					Weight: 25,
				},
			},
		},
	}

	for _, tt := range cases {
		t.Run(tt.name, func(t *testing.T) {

  OpSet op_set_;

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

// If the weight is applicable to dynamic range quantization, insert Quantize
// and Dequantize ops with per-tensor scale.
class PrepareDRQQuantizableOp : public OpRewritePattern<arith::ConstantOp> {
 public:

  SPARSE = 1,
  DENSE = 2,
}

table LSHProjectionOptions {
  type: LSHProjectionType;
}

table SVDFOptions {
  rank:int;
  fused_activation_function:ActivationFunctionType;
  // For weights-only quantization, use asymmetric quantization for non
  // constant inputs at evaluation time.
  asymmetric_quantize_inputs:bool;
}

// An implementation of TensorFlow RNNCell.
table RNNOptions {

	wg := sync.WaitGroup{}

	clientLatencies := make([]timeseries.Data, len(s.mesh.Clients()))
	for i, client := range s.mesh.Clients() {
		i := i
		client := client
		wg.Add(1)
		go func() {
			// Assign weights to the endpoints.
			var conns []*loadbalancer.WeightedConnection
			for _, n := range s.mesh.Nodes() {
				conns = append(conns, s.newWeightedConnection(client, n))
			}

			// Create a load balancer

          &q_builder, input_model, quantized_type, use_updated_hybrid_scheme,
          ::tflite::optimize::QuantizerType::OLD_QUANTIZER) != kTfLiteOk) {
    return absl::InvalidArgumentError(
        "Quantize weights transformation failed.");
  }
  const uint8_t* q_buffer = q_builder.GetBufferPointer();
  *result =
      std::string(reinterpret_cast<const char*>(q_buffer), q_builder.GetSize());

  return absl::OkStatus();
}

}

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)
		}
	}
}