}
    }
  }
}
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 {

  const bool disable_per_channel_;

  // We should distinguish weights and bias constants. Biases are specified by
  // the quantization spec or are the operands of ops with same scale spec. The
  // rest are weights.
  DenseSet<Operation*> weights_;

  // The weights require narrow_range quantization. This map collects all the
  // weight operands defined by the op quant spec. The value of each entry is

namespace {

using ::stablehlo::quantization::Method;
using ::stablehlo::quantization::QuantizedType;
using ::stablehlo::quantization::WeightOnlyPtq;

// Inserts quantization parameters of weights for weight-only quantization and
// dynamic range quantization of `stablehlo.convolution` and
// `stablehlo.dot_general`.
class InsertWeightParamPass
    : public impl::InsertWeightParamPassBase<InsertWeightParamPass> {
 public:

      "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.

    // This is the argument used to refer to the pass in
    // the textual format (on the commandline for example).
    return "quant-quantize-weights";
  }

  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Quantize weights used by quantizable ops.";
  }

  void getDependentDialects(DialectRegistry& registry) const override {

  // 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);

	if forwardTo == nil {
		return nil, nil, nil
	}

	weights := []int{}
	action := []k8s.BackendRef{}
	for _, w := range forwardTo {
		wt := int(ptr.OrDefault(w.Weight, 1))
		if wt == 0 {
			continue
		}
		action = append(action, w)
		weights = append(weights, wt)
	}
	if len(weights) == 1 {
		weights = []int{0}
	}

	var invalidBackendErr *ConfigError

// storage type. The available values use the full range of the storage value,
// i.e. [-128, 127]. Assumes asymmetric quantization, meaning the zero point
// value can be a non-zero value.
// If `narrow_range` is set true (ex: for weights), a restricted range of
// integers will be used for symmetric mapping, i.e. [-127, 127].
UniformQuantizedType CreateI8F32UniformQuantizedType(Location loc,
                                                     MLIRContext& context,

				weights := make([]int, 0)
				for _, localityEndpoint := range cluster.LoadAssignment.Endpoints {
					weights = append(weights, int(localityEndpoint.LoadBalancingWeight.GetValue()))
				}
				if !reflect.DeepEqual(weights, tt.expected) {
					t.Errorf("Got weights %v expected %v", weights, tt.expected)
				}
			})
		}
	})

// point constant.
def : Pat<(TFL_DequantizeOp
             (TFL_QuantizeOp (Arith_ConstantOp F32ElementsAttr:$cst), $qt)),
          (TFL_ConstOp $cst)>;

// Transpose conv supports hybrid computation with quantized weights.
def FoldQuantWeightsIntoTposeConv : Pat<
  (TFL_TransposeConvOp
    $output_shape,
    (TFL_DequantizeOp $quant_weights),
    $quant_input,
    $bias, $padding, $stride_h, $stride_w, $faf),