namespace tensorflow {
namespace {

std::string ConvertToMlirString(const std::vector<int64_t>& dims,
                                bool unknown_rank, DataType dtype) {
  TensorShapeProto shape;
  shape.set_unknown_rank(unknown_rank);
  for (int64_t dim : dims) {
    shape.add_dim()->set_size(dim);
  }
  mlir::MLIRContext context;
  mlir::Builder b(&context);

  auto dims_type =
      RankedTensorType::get(/*shape=*/{1}, rewriter.getIntegerType(64));
  auto dims = DenseIntElementsAttr::get(dims_type, {feature_dim});
  if (shape_value) {
    return rewriter.createOrFold<mhlo::DynamicBroadcastInDimOp>(
        loc, result_type, value1d, shape_value, dims);
  }
  assert(result_type.hasStaticShape());
  return rewriter.create<mhlo::BroadcastInDimOp>(loc, result_type, value1d,

constexpr char longTagParam[] = "LONGTAG____________________________";
constexpr float largeValueParam = 2352352.2623433;

#define BM_ScalarSummaryDev(device, dims, name, tag, value)                 \
  void BM_ScalarSummary##name##device(::testing::benchmark::State& state) { \
    TensorShape tensorshape(DIMARGS dims);                                  \
    auto g = BM_ScalarSummaryOp(tensorshape, #tag, value);                  \

    %11 = stablehlo.convert %3 : (tensor<1x1x3xi32>) -> tensor<1x1x3xf32>
    %12 = stablehlo.broadcast_in_dim %11, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional
    %13 = stablehlo.subtract %10, %12 : tensor<1x4x3xf32>  // Precalculated zp_neg.
    %14 = stablehlo.broadcast_in_dim %4, dims = [0, 1, 2] : (tensor<1x1x3xf32>) -> tensor<1x4x3xf32>  // Optional
    %15 = stablehlo.multiply %13, %14 : tensor<1x4x3xf32>  // s1 * s2

# See the OWNERS docs at https://go.k8s.io/owners

reviewers:
  - johnSchnake
approvers:
  - dims
  - cblecker

# See the OWNERS docs at https://go.k8s.io/owners

reviewers:
  - smarterclayton
  - deads2k
  - caesarxuchao
  - liggitt

}

// CHECK-DAG: %[[CONST:.+]] = stablehlo.constant {{.*}} : tensor<8x8x3x3xf32>
// CHECK-DAG: %[[TRANSPOSE_0:.+]] = stablehlo.transpose %[[ARG]], dims = [0, 2, 3, 1] : (tensor<1x8x4x4xf32>) -> tensor<1x4x4x8xf32>
// CHECK-DAG: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %[[CONST]], dims = [2, 3, 1, 0] : (tensor<8x8x3x3xf32>) -> tensor<3x3x8x8xf32>

                                     /*len=*/sizeof(value),
                                     /*deleter=*/[](void*, size_t) {}, &status);
  ASSERT_TRUE(status.ok()) << status.message();

  EXPECT_EQ(tensor.dims(), 0);
  EXPECT_EQ(tensor.dtype(), dtype);
  EXPECT_EQ(*reinterpret_cast<typename TypeParam::type*>(tensor.data()), 42);
  EXPECT_EQ(tensor.num_bytes(), sizeof(typename TypeParam::type));
  EXPECT_EQ(tensor.num_elements(), 1);

reviewers:
  - thockin
  - smarterclayton
  - wojtek-t
  - deads2k
  - derekwaynecarr
  - caesarxuchao
  - mikedanese
  - liggitt
  - janetkuo
  - ncdc

approvers:
  - deads2k
  - liggitt
reviewers:
  - thockin
  - smarterclayton
  - yujuhong
  - derekwaynecarr
  - mikedanese
  - dchen1107
  - justinsb
  - dims
  - andrewsykim