}
	}
}

func TestSorted(t *testing.T) {
	s := Sorted(Values(ints[:]))
	if !IsSorted(s) {
		t.Errorf("sorted %v", ints)
		t.Errorf("   got %v", s)
	}
}

func TestSortedFunc(t *testing.T) {
	s := SortedFunc(Values(ints[:]), func(a, b int) int { return a - b })
	if !IsSorted(s) {
		t.Errorf("sorted %v", ints)
		t.Errorf("   got %v", s)
	}
}

func TestSortedStableFunc(t *testing.T) {

	assert(inst != nil)

	ctxt.mu.Lock()
	defer ctxt.mu.Unlock()

	for _, e := range ctxt.typeMap[h] {
		if inst == nil || Identical(inst, e.instance) {
			return e.instance
		}
		if debug {
			// Panic during development to surface any imperfections in our hash.
			panic(fmt.Sprintf("%s and %s are not identical", inst, e.instance))
		}
	}

namespace tensorflow {

// Extracts the attributes of a MLIR operation and populates the converted
// attributes in a proto map<string, AttrValue>.
Status GetAttrValuesFromOperation(
    mlir::Operation* inst, llvm::StringRef name,
    const tensorflow::OpRegistrationData* op_reg_data,
    bool ignore_unregistered_attrs, AttrValueMap* attributes);

// Converts a MLIR operation to TensorFlow NodeDef with given node name. This

        for (int i = 0; i < minLength; i++) {
            int result = Ints.compare(this.components[i], that.components[i]);
            if (result != 0) {
                return result;
            }
        }
        return Ints.compare(this.components.length, that.components.length);
    }

	return nil
}

// disassemble parses the output of the objdump command and returns
// the assembly instructions in a slice.
func disassemble(asm []byte) ([]plugin.Inst, error) {
	buf := bytes.NewBuffer(asm)
	function, file, line := "", "", 0
	var assembly []plugin.Inst
	for {
		input, err := buf.ReadString('\n')
		if err != nil {
			if err != io.EOF {
				return nil, err
			}
			if input == "" {
				break
			}

	if b != 4 {
		panic(b)
	}
	_ = i()
	if call != "ffgfgi" {
		panic(call)
	}
	if c4 != 4 {
		panic(c4)
	}

	out := ""
	for _, s := range ints {
		out += s
	}
	if out != "123" {
		panic(out)
	}

	sum := 0
	for s := range ints {
		sum += s
	}
	if sum != 3 {
		panic(sum)
	}

	// go.tools/ssa/interp cannot support unsafe.Pointer.
	if os.Getenv("GOSSAINTERP") == "" {

				continue Search
			}
			args[j] = arg
		}

		decoderCover[i] = true

		inst = Inst{
			Op:   op,
			Args: args,
			Enc:  x,
			Len:  4,
		}
		priority = f.priority
		continue Search
	}
	if inst.Op != 0 {
		return inst, nil
	}
	return Inst{}, errUnknown
}

// An instArg describes the encoding of a single argument.

  }
  auto& module = module_or.value();
  std::srand(0);
  for (auto fn : module->getOps<mlir::func::FuncOp>()) {
    for (auto& bb : fn) {
      for (auto& inst : bb) {
        auto attr_id = mlir::StringAttr::get(context, "value");
        if (auto attr = inst.getAttrOfType<mlir::ElementsAttr>(attr_id)) {
          mlir::Attribute rand_val;
          mlir::Type element_type = attr.getShapedType().getElementType();

          notValues: ["not-presenter", "not-presenter-prefix-*", "*-not-suffix-presenter", "*"]
        - key: "request.auth.claims[iss]"
          values: ["iss", "iss-prefix-*", "*-suffix-iss", "*"]
          notValues: ["not-iss", "not-iss-prefix-*", "*-not-suffix-iss", "*"]
        - key: "request.auth.claims[nested1][nested2]"
          values: ["nested", "nested-prefix-*", "*-suffix-nested", "*"]

// Converts an MLIR operation to TensorFlow NodeDef with given node name. This
// name should be unique to the graph it is being inserted into.
absl::StatusOr<std::unique_ptr<NodeDef>> GetOperationNodeDef(
    mlir::Operation* inst, llvm::StringRef name);

// Converts MLIR attributes with values to their tensorflow equivalent.
// "name" and "device" attributes are ignored by default. Use attrs_to_ignore to