}
  }
  // Verify axisStats (optional) attribute.
  if (getAxisStats()) {
    if (!getAxis()) return emitOpError("axis must be specified for axisStats");

    auto shape = tensorArg.getShape();
    auto argSliceSize =
        std::accumulate(std::next(shape.begin(), *getAxis()), shape.end(), 1,
                        std::multiplies<int64_t>());

    auto axisStatsType = getAxisStats()->getShapedType();

  func.eraseBody();
  func.addEntryBlock();
  ImplicitLocOpBuilder builder(func.getLoc(), func.getBody());
  llvm::SmallVector<int32_t> result_shape_i32;
  auto result_shape = result_ty.getShape();
  for (auto element : result_shape) {
    result_shape_i32.push_back(static_cast<int32_t>(element));
  }
  auto result_shape_attr = builder.getI32TensorAttr(result_shape_i32);
  Value result_shape_tensor =

absl::StatusOr<arith::ConstantOp> CreateConstOpWithVectorValue(
    PatternRewriter* rewriter, Location loc, ShapedType shaped_type,
    int value) {
  ShapedType dense_type = RankedTensorType::get(shaped_type.getShape(),
                                                shaped_type.getElementType());
  auto attr = CreateTypedAttr(dense_type, value);

  return rewriter->create<arith::ConstantOp>(loc, dense_type,

      std::vector<float> scales_inv;
      std::vector<int32_t> dimension;
      dimension.insert(dimension.end(), new_dense_type.getShape().begin(),
                       new_dense_type.getShape().end());
      std::transform(uniform_type.getScales().begin(),
                     uniform_type.getScales().end(),
                     std::back_inserter(scales_inv),

  TF_RETURN_IF_ERROR(
      ConvertScalarTypeToDataType(type.getElementType(), &dtype));

  if (!type.hasRank())
    return std::make_pair(dtype, tensorflow::PartialTensorShape());

  auto shape = type.getShape();
  llvm::SmallVector<int64_t, 4> dims;
  dims.assign(shape.begin(), shape.end());
  return std::make_pair(dtype, tensorflow::PartialTensorShape(dims));
}

}  // namespace

  // equal.
  TensorType GetTransposedTensorType(
      const TensorType type, const ArrayRef<int64_t> permutation) const {
    const SmallVector<int64_t> after_shape =
        Permute<int64_t>(type.getShape(), permutation);
    return type.cloneWith(after_shape, type.getElementType());
  }
};

}  // namespace

void NchwConvolutionToNhwcPass::runOnOperation() {
  func::FuncOp func_op = getOperation();

    if (!input_type) {
      return failure();
    }
    const ArrayRef<int64_t> input_shape = input_type.getShape();

    const int input_size = input_shape.size();

    RankedTensorType begin_type = mlir::cast<RankedTensorType>(begin.getType());
    const ArrayRef<int64_t> begin_shape = begin_type.getShape();
    const int begin_dim = begin_shape.size();

    if (begin_dim != 1) return failure();

  }
  return true;
}

bool IsDimensionsDegenerateExceptLastOne(TypedAttr val) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(val.getType())) {
    return IsDimensionsDegenerateExceptLastOne(ranked_type.getShape());
  }
  return false;
}

}  // namespace TFL

namespace tensorflow {
namespace tfrt_compiler {
namespace {

constexpr int64_t kDefaultCheapCost = 1;

int64_t GetRankedTensorSize(mlir::TensorType type) {
  auto shape = type.getShape();

  int64_t size = 1;
  for (int64_t dim : shape) {
    // For unknown dimensions, use 1 as the size because it is usually the batch
    // dimension.
    //

                                                    ArrayRef<int64_t> perm,
                                                    bool isInvert) {
  SmallVector<int64_t, 4> permutedShape = applyPermutation(
      type.getShape(), isInvert ? invertPermutationVector(perm) : perm);
  return RankedTensorType::get(permutedShape, type.getElementType());
}

static RankedTensorType GetInvertPermutedTensorType(RankedTensorType type,