%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},

  TestHandleFromDLPack(status, ctx, {4}, {1});
  TestHandleFromDLPack(status, ctx, {4, 3, 2}, {});
  TestHandleFromDLPack(status, ctx, {4, 3, 2}, {6, 2, 1});
  // Test that dims with size=1 can have any stride.
  TestHandleFromDLPack(status, ctx, {1}, {1});
  TestHandleFromDLPack(status, ctx, {1}, {0});
  TestHandleFromDLPack(status, ctx, {4, 1, 2}, {2, 1, 1});
  TestHandleFromDLPack(status, ctx, {4, 1, 2}, {2, 0, 1});

  %cst = arith.constant dense<4> : tensor<i32>
  %stride = "tf.Add"(%arg2, %cst) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  // CHECK: [[Result:%.*]]:3 = "tf.While"(%arg0, %arg1, %{{.+}} <{body = @tf.WhileRegion_body, cond = @tf.WhileRegion_cond
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) (
    {
      ^bb0(%carg0: tensor<*xf32>, %carg1: tensor<i32>):
      %ne = "tf.NotEqual"(%carg1, %stride) : (tensor<i32>, tensor<i32>) -> tensor<i1>

  auto transform_result_type =
      RankedTensorType::get(transform_shape, getElementTypeOrSelf(input));
  input.setType(transform_result_type);
  op.setOperand(0, input);
  return success();
}

// Handles stride for the first convolution for the transform.
void HandleConv2DStride(TF::Conv2DOp conv2d) {
  MLIRContext* context = conv2d.getContext();
  SmallVector<int64_t, 4> values = {1, 1, 1, 1};

// TFE_NewTensorHandleFromDeviceMemory.
void DeallocatorWrapperFunc(void* data, size_t len, void* dlmt_vptr) {
  TFE_CallDLManagedTensorDeleter(dlmt_vptr);
}

// Checks whether the stride array matches the layout of compact, row-majored
// data.
bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {
  bool valid = true;

					if !inclusive {
						if v == minSignedValue(limit.Type) {
							return false // < minint is never satisfiable.
						}
						v--
					}
					if init.isGenericIntConst() {
						// Use stride to compute a better lower limit.
						if init.AuxInt > v {
							return false
						}
						v = addU(init.AuxInt, diff(v, init.AuxInt)/uint64(step)*uint64(step))
					}
					if addWillOverflow(v, step) {