}
  }

  const DimensionVector& batch_dimensions() const { return batch_dimensions_; }
  const DimensionVector& contracting_dimensions() const {
    return contracting_dimensions_;
  }
  // Out dimensions are any dimensions that are neither batch nor contracting
  // dimensions, hence will be propagated to output shape.
  const DimensionVector& out_dimensions() const { return out_dimensions_; }

}

bool CanFuseConvOrDepthwiseConvShapes(const ArrayRef<int64_t> filter_shape,
                                      const ArrayRef<int64_t> elements_shape,
                                      bool is_depthwise) {
  // Val tensor must be a scalar or of a shape [1, ... , 1, elements_depth].
  const int elements_rank = elements_shape.size();
  for (int i = 0; i < elements_rank - 1; ++i) {

// `high` if `high` is less than `val`; otherwise returns `val`.
template <class T>
constexpr const T &Clamp(const T &val, const T &low, const T &high) {
  assert(!(high < low));
  return (val < low) ? low : (high < val) ? high : val;
}

// Checks if the `index` bit of `val` is set.
template <class T>
constexpr bool IsSet(const T &val, unsigned index) {
  return (val & (1 << index)) != 0;
}

  %feature_group_count = "tf.Const"() {value = dense<1> : tensor<2xi32>} : () -> tensor<2xi32>
  %lhs_dilation = "tf.Const"() {value = dense<[4, 1, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %rhs_dilation = "tf.Const"() {value = dense<1> : tensor<3xi32>} : () -> tensor<3xi32>
  %padding = "tf.Const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>

    llvm::SmallVectorImpl<ShapedTypeComponents>& inferredReturnShapes) {
  const int64_t num_spatial_dims = std::is_same<OpT, Conv2DOpAdaptor>() ? 2 : 3;
  const int64_t num_dims = 2 + num_spatial_dims;
  const Value input = op.getInput();
  const Value filter = op.getFilter();
  const TensorType input_ty = mlir::cast<TensorType>(input.getType());
  const TensorType filter_ty = mlir::cast<TensorType>(filter.getType());

	EI_PAD        = 9  /* Start of padding (per SVR4 ABI). */
	EI_NIDENT     = 16 /* Size of e_ident array. */
)

// Initial magic number for ELF files.
const ELFMAG = "\177ELF"

// Version is found in Header.Ident[EI_VERSION] and Header.Version.
type Version byte

const (
	EV_NONE    Version = 0
	EV_CURRENT Version = 1
)

var versionStrings = []intName{
	{0, "EV_NONE"},
	{1, "EV_CURRENT"},
}

func testTransportGzip(t *testing.T, mode testMode) {
	if mode == http2Mode {
		t.Skip("https://go.dev/issue/56020")
	}
	const testString = "The test string aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
	const nRandBytes = 1024 * 1024
	ts := newClientServerTest(t, mode, HandlerFunc(func(rw ResponseWriter, req *Request) {
		if req.Method == "HEAD" {

}

func TestHeadBody(t *testing.T) {
	const identityMode = false
	const chunkedMode = true
	run(t, func(t *testing.T, mode testMode) {
		t.Run("identity", func(t *testing.T) { testHeadBody(t, mode, identityMode, "HEAD") })
		t.Run("chunked", func(t *testing.T) { testHeadBody(t, mode, chunkedMode, "HEAD") })
	})
}

func TestGetBody(t *testing.T) {
	const identityMode = false
	const chunkedMode = true

import "golang.org/x/text/internal/tag"

// CLDRVersion is the CLDR version from which the tables in this package are derived.
const CLDRVersion = "32"

const NumLanguages = 8798

const NumScripts = 261

const NumRegions = 358

type FromTo struct {
	From uint16
	To   uint16
}

const nonCanonicalUnd = 1201
const (
	_af  = 22
	_am  = 39
	_ar  = 58
	_az  = 88
	_bg  = 126
	_bn  = 165
	_ca  = 215
	_cs  = 250

  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

func.func @WhileOp_body(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>) {
  %cst = arith.constant dense<1> : tensor<i32> loc("Const1")