var i32_0, i32_1 int32
		var m8, m9, m10 map[bool]struct {
		}
		pi64_1 = pi64_0
		m6[func(G3, G2) G1 {
			return (G1(35) | i8_0) << i8_1 / i8_1 &^ i8_1 / i8_1
		}(G3("YBiKg"), G2(122.6))] = make(chan bool)
		ast0 = ast0
		i8_1 = (((G1(10)+i8_1)&i8_0+i8_0)&i8_0&i8_1 ^ i8_1) & i8_1
		am4 = am3
		i32_1 = int32(10) &^ i32_0
		m8[func(float64, G3) bool {
			return func(rune, int16) bool {

// Inverse Hilbert matrix
const (
	i0_0 = +1 * b2_1 * b2_1 * b0_0 * b0_0
	i0_1 = -2 * b2_0 * b3_1 * b1_0 * b1_0

	i1_0 = -2 * b3_1 * b2_0 * b1_1 * b1_1
	i1_1 = +3 * b3_0 * b3_0 * b2_1 * b2_1
)

// Product matrix
const (
	p0_0 = h0_0*i0_0 + h0_1*i1_0
	p0_1 = h0_0*i0_1 + h0_1*i1_1

	p1_0 = h1_0*i0_0 + h1_1*i1_0
	p1_1 = h1_0*i0_1 + h1_1*i1_1
)

// Verify that product is identity matrix

  Output id_1 = ops::Identity(root.WithOpName("id_1"), outputs[1]);

  FixupSourceAndSinkEdges(root.graph());

  PredicateMapTy predicate_map;
  TF_ASSERT_OK(ComputePredicates(*root.graph(), &predicate_map));

  EXPECT_EQ(predicate_map[ControlOutputFor(id_0)], "#false");
  EXPECT_EQ(predicate_map[ControlOutputFor(id_1)], "#true");
}

	i8 Int8,
	i16 Int16,
	i32 Int32,
	i64 Int64,
	i8_16 Int8_16,
) {
	b = 42
	b = 42.0
	// etc.

	i8 = -1 << 7
	i8 = 1<<7 - 1
	i16 = -1 << 15
	i16 = 1<<15 - 1
	i32 = -1 << 31
	i32 = 1<<31 - 1
	i64 = -1 << 63
	i64 = 1<<63 - 1

	i8_16 = -1 << 7
	i8_16 = 1<<7 - 1
	i8_16 = - /* ERRORx `cannot use .* as Int8_16` */ 1 << 15
	i8_16 = 1 /* ERRORx `cannot use .* as Int8_16` */ <<15 - 1
}

  i32_max_ = QuantizedType::getDefaultMaximumForInteger(kSigned, k32Bits);
  any_ = AnyQuantizedType();
  qi8_ = AnyQuantizedType::get(kSigned, i8_, f32_, i8_min_, i8_max_);
  qi8n_ = AnyQuantizedType::get(kSigned, i8_, f32_, i8_min_ + 1, i8_max_);
  qi32_ = AnyQuantizedType::get(kSigned, i32_, f32_, i32_min_, i32_max_);
  assert(qi8n_ == qi8n_);
}

std::optional<KernelSpec> DeviceTarget::GetKernelSpec(

  !tfr.attr {tfr.name="K",tfr.type="dtype"}) -> !tfr.tensor<K> attributes {T, K}
tfr.func @tf__positive_(!tfr.tensor<T>) -> !tfr.tensor<i1_> attributes {T, i1_}
tfr.func @tf__invalid_type_op_(!tfr.tensor<T>) -> !tfr.tensor<i8_> attributes {T, i8_}

// CHECK-LABEL: decompose_tf_same
func.func @decompose_tf_same(%arg0: tensor<1x2x3x4x!tf_type.string>) -> tensor<1x2x3x4x!tf_type.string> {
  %0 = "tfr.cast"(%arg0) : (tensor<1x2x3x4x!tf_type.string>) -> !tfr.tensor

      QuantizedMultipliers* output_multipliers, QuantizedRanges* output_ranges);

  // A set of parameters are required to build the signatures.
  FloatType f32_;
  IntegerType i8_, i32_;
  int64_t i8_min_, i8_max_, i32_min_, i32_max_;
  quant::AnyQuantizedType any_, qi8_, qi8n_, qi32_;

 private:
  // Maps the kernel names to all the available kernels.
  llvm::StringMap<KernelSpecs> specs_;

	for {
		i := v_0
		if i.Op != OpRsh16x64 {
			break
		}
		_ = i.Args[1]
		x := i.Args[0]
		i_1 := i.Args[1]
		if i_1.Op != OpConst64 {
			break
		}
		c := auxIntToInt64(i_1.AuxInt)
		if v_1.Op != OpConst64 || auxIntToInt64(v_1.AuxInt) != c || !(c >= 0 && c < 16 && i.Uses == 1) {
			break
		}
		v.reset(OpAnd16)