// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=weight_only target-opset=XLA' -quant-quantize-composite-functions='quantization-method=weight_only target-opset=XLA enable-per-channel-quantization=true' -symbol-dce | FileCheck --c...

QuantizedType CalculateUniformQuantParams(
    PatternRewriter& rewriter, TF::ConstOp op,
    tensorflow::quantization::QuantizationComponentSpec& weight_spec) {
  // TODO - b/278949920: Enable Per-Channel Quantization for XLA Opset
  // Currently, support symmetric, per-tensor, signed int8
  const bool kIsNarrowRange = true;
  const bool kIsSigned = true;
  const int kBitWidth = 8;

  DenseFPElementsAttr attr;

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -quant-quantize-composite-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -symbol-dce | FileCheck %s

module {
  // TODO(b/260020937): Support transpose_a, transpose_b for matmul.
  func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) {

		spcs := &spcSpace[i]
		if spcs.lo <= spc && spc < spcs.hi {
			return spcs.SPCconv(spc)
		}
	}
	return fmt.Sprintf("SPC???%d", spc)
}

type opSet struct {
	lo    As
	names []string
}

// Not even worth sorting
var aSpace []opSet

// RegisterOpcode binds a list of instruction names
// to a given instruction number range.
func RegisterOpcode(lo As, Anames []string) {

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='quantization-method=ptq target-opset=XLA' | FileCheck %s
// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='quantization-method=ptq target-opset=XLA enable-per-channel-quantization=true' | FileCheck --check-prefix=PerChannel %s

module {

// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-quantize='target-opset=XLA' -verify-each=false | FileCheck %s

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>

  ShapedType input_shape =
      mlir::dyn_cast<ShapedType>(op->getOperand(0).getType());
  if (!input_shape) {
    return op->emitError(
        "Only input with known shape is supported for Uniform Quantized "
        "opset.");
  }

  if (op->getParentOfType<func::FuncOp>().getName().contains("depthwise_")) {
    feature_group_cnt = input_shape.getDimSize(3);
  }

  attrs.push_back(rewriter.getNamedAttr(

	}
}

// Handle children
func (po *poset) setchl(i uint32, l posetEdge) { po.nodes[i].l = l }
func (po *poset) setchr(i uint32, r posetEdge) { po.nodes[i].r = r }
func (po *poset) chl(i uint32) uint32          { return po.nodes[i].l.Target() }
func (po *poset) chr(i uint32) uint32          { return po.nodes[i].r.Target() }
func (po *poset) children(i uint32) (posetEdge, posetEdge) {
	return po.nodes[i].l, po.nodes[i].r

	ListSets() ([]string, error)
	// GetVersion returns the "X.Y" version string for ipset.
	GetVersion() (string, error)
}

// IPSetCmd represents the ipset util. We use ipset command for ipset execute.
const IPSetCmd = "ipset"

// EntryMemberPattern is the regular expression pattern of ipset member list.
// The raw output of ipset command `ipset list {set}` is similar to,
// Name: foobar
// Type: hash:ip,port
// Revision: 2

	testCases := []struct {
		ipset     *IPSet
		expectErr bool
		desc      string
	}{
		{ // case[0]
			ipset: &IPSet{
				Name:       "test",
				SetType:    HashIPPort,
				HashFamily: ProtocolFamilyIPV4,
				HashSize:   1024,
				MaxElem:    1024,
			},
			expectErr: false,
			desc:      "No Port range",
		},
		{ // case[1]
			ipset: &IPSet{
				Name:       "SET",