if (!dot.getLhs().getType().hasStaticShape() ||
        !dot.getRhs().getType().hasStaticShape())
      return rewriter.notifyMatchFailure(
          dot, "all dot_general operands must be statically shaped");
    if (dot.getLhs().getType().getElementType() !=
            first_dot.getLhs().getType().getElementType() ||
        dot.getRhs().getType().getElementType() !=

  StringAttr conv_padding = op.getPaddingAttr();
  SmallVector<int64_t> padding_nums;
  ShapedType lhs_shape = mlir::cast<ShapedType>(op.getLhs().getType());
  ShapedType rhs_shape = mlir::cast<ShapedType>(op.getRhs().getType());

  // Handle only static shape cases.
  // TODO(b/260284866): Handle dynamic shape cases.
  if (!lhs_shape.hasStaticShape()) {

    if (!mlir::cast<ShapedType>(conv_op.getRhs().getType()).hasStaticShape()) {
      return false;
    }
    if (!mlir::cast<ShapedType>(conv_op.getLhs().getType()).hasStaticShape() &&
        !mlir::cast<ShapedType>(conv_op.getType()).hasStaticShape()) {
      auto dnums = conv_op.getDimensionNumbers();
      auto lhs_type = mlir::cast<ShapedType>(conv_op.getLhs().getType());

}

template <typename Op, typename ElementType = Type, typename ValType,
          typename Convert>
static Attribute BinaryFolder(Op *op) {
  auto lhs_op = op->getLhs().template getDefiningOp<mhlo::ConstantOp>();
  auto rhs_op = op->getRhs().template getDefiningOp<mhlo::ConstantOp>();
  if (!lhs_op || !lhs_op) return {};

  auto lhs = dyn_cast_or_null<DenseElementsAttr>(lhs_op.getValue());

    Value constant_val = add_op ? add_op.getRhs() : sub_op.getRhs();
    if (!matchPattern(constant_val, m_Constant(&added_value))) return failure();

    // Check the add op is applied to begin.
    mlir::TypedValue<::mlir::TensorType> begin_tensor =
        strided_slice_op.getBegin();
    mlir::TypedValue<::mlir::TensorType> add_source_tensor =
        add_op ? add_op.getLhs() : sub_op.getLhs();
    if (begin_tensor != add_source_tensor) {

  }

  const auto input_type = op.getLhs().getType().cast<TensorType>();
  if (input_type.getRank() != 4) {
    LLVM_DEBUG(llvm::dbgs() << "Only 2D convolution op is supported. "
                               "Expected input rank of 4. Got: "
                            << input_type.getRank() << ".\n");
    return failure();
  }

  const auto filter_type = op.getRhs().getType().cast<TensorType>();

  return ConvertDot(
      rewriter, dot_op.getLhs(), dot_op.getRhs(), dot_dimension_numbers,
      mlir::cast<ShapedType>(dot_op.getResult().getType()), dot_op.getLoc());
}

Value ConvertDotGeneralOp(PatternRewriter& rewriter, Operation* old_op) {
  auto dot_general_op = cast<mhlo::DotGeneralOp>(old_op);
  return ConvertDot(
      rewriter, dot_general_op.getLhs(), dot_general_op.getRhs(),
      dot_general_op.getDotDimensionNumbers(),

# Get the sha of top commit
# $1 = repo
function getSha() {
  git ls-remote "https://github.com/istio/${1}.git" "refs/heads/${UPDATE_BRANCH}" | cut -f 1
}

make update-common

export GO111MODULE=on
go get -u "istio.io/api@${UPDATE_BRANCH}"
go get -u "istio.io/client-go@${UPDATE_BRANCH}"
go mod tidy

sed -i "s/^BUILDER_SHA=.*\$/BUILDER_SHA=$(getSha release-builder)/" prow/release-commit.sh

                @Inject abstract ProviderFactory getProviderFactory()

                @Internal abstract Property<String> getOther()

                @Internal Provider<String> getFoo() {
                    return other;
                }

                @TaskAction
                void useIt() {
                    assert foo.get() == "foobar"
                }
            }

    auto max_op = dyn_cast_or_null<MaxOp>(
        return_op.getOperands().front().getDefiningOp());
    if (!max_op) return false;

    return (max_op.getLhs() == block.getArgument(0)) &&
           (max_op.getRhs() == block.getArgument(1));
  }

  // Whether `op` has the `padding` attribute (which is optional).
  bool HasPadding(mlir::stablehlo::ReduceWindowOp op) const {