for _, xr := range x.R16 {
		lo, hi, stride := rune(xr.Lo), rune(xr.Hi), rune(xr.Stride)
		if stride == 1 {
			r = appendRange(r, lo, hi)
			continue
		}
		for c := lo; c <= hi; c += stride {
			r = appendRange(r, c, c)
		}
	}
	for _, xr := range x.R32 {
		lo, hi, stride := rune(xr.Lo), rune(xr.Hi), rune(xr.Stride)
		if stride == 1 {
			r = appendRange(r, lo, hi)
			continue
		}

        Value step_cst =
            rewriter.create<arith::ConstantIndexOp>(loc, step.getSExtValue());
        Value stride = rewriter.create<arith::MulIOp>(loc, step_cst, iter);
        Value iv_unroll =
            rewriter.create<arith::AddIOp>(loc, mapping.lookup(iv), stride);
        mapping.map(iv, iv_unroll);
      }

      Operation *terminator_op;

  %0 = mhlo.composite "aten.avg_pool2d.default" %arg0 {composite_attributes = {ceil_mode = false, count_include_pad = true, divisor_override = "py_None", kernel_size = dense<3> : tensor<2xi64>, padding = dense<0> : tensor<2xi64>, stride = dense<1> : tensor<2xi64>}, decomposition = @XlaCallModule_aten.avg_pool2d.default.impl_0} : (tensor<1x3x6x6xf32>) -> tensor<1x3x4x4xf32>
  %1 = "tf.Identity"(%0) {device = ""} : (tensor<1x3x4x4xf32>) -> tensor<*xf32>

Attribute GetWindowStridesValue(
    PatternRewriter& rewriter, llvm::StringMap<Attribute>& identifier_to_attr) {
  ArrayAttr stride = mlir::dyn_cast<ArrayAttr>(identifier_to_attr["strides"]);
  const int stride_h = mlir::cast<IntegerAttr>(stride[1]).getInt();
  const int stride_w = mlir::cast<IntegerAttr>(stride[2]).getInt();
  return rewriter.getI64ArrayAttr({stride_h, stride_w});
}

Attribute GetLhsDilationValue(PatternRewriter& rewriter,

	}
	return
}

func testReadLine(t *testing.T, input []byte) {
	//for stride := 1; stride < len(input); stride++ {
	for stride := 1; stride < 2; stride++ {
		done := 0
		reader := testReader{input, stride}
		l := NewReaderSize(&reader, len(input)+1)
		for {
			line, isPrefix, err := l.ReadLine()
			if len(line) > 0 && err != nil {
				t.Errorf("ReadLine returned both data and error: %s", err)
			}

  %3 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {stride = [1, 1], pad = [[1, 1], [1, 1]], rhs_dilate = [1, 1]}...

              mlir::cast<IntegerAttr>(op.getRhsDilationAttr()[i]).getInt());

      const int64_t output_size = (lhs_size_dilated + stride - 1) / stride;
      const int64_t total_padding = std::max(
          (output_size - 1) * stride + rhs_size_dilated - lhs_size_dilated,
          static_cast<int64_t>(0));
      const int64_t padding_begin = total_padding / 2;

pkgpath unicode;
init unicode unicode..import;
var ASCII_Hex_Digit <type 1 *<type 2 "RangeTable" <type 3 struct { R16 <type 4 [] <type 5 "Range16" <type 6 struct { Lo <type -6>; Hi <type -6>; Stride <type -6>; }>>>; R32 <type 7 [] <type 8 "Range32" <type 9 struct { Lo <type -7>; Hi <type -7>; Stride <type -7>; }>>>; LatinOffset <type -11>; }>>>;
var Adlam <type 1>;
var Ahom <type 1>;
var Anatolian_Hieroglyphs <type 1>;
var Arabic <type 1>;
var Armenian <type 1>;

// RUN: tf-opt %s -tf-layout-assignment=force-data-format=NHWC -verify-diagnostics | FileCheck %s --dump-input=always

// IMPORTANT: Tensor shapes do not match convolution parameters (stride,
// dilations, etc...). This test only verifies that changing convolution data
// layout will update all the attributes.

// CHECK-LABEL: func @transposeConv2D
func.func @transposeConv2D(%input: tensor<1x3x32x32xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32> {

		{"Alpha.Pix", Field, 0},
		{"Alpha.Rect", Field, 0},
		{"Alpha.Stride", Field, 0},
		{"Alpha16", Type, 0},
		{"Alpha16.Pix", Field, 0},
		{"Alpha16.Rect", Field, 0},
		{"Alpha16.Stride", Field, 0},
		{"Black", Var, 0},
		{"CMYK", Type, 5},
		{"CMYK.Pix", Field, 5},
		{"CMYK.Rect", Field, 5},
		{"CMYK.Stride", Field, 5},
		{"Config", Type, 0},
		{"Config.ColorModel", Field, 0},