type T40 struct {
	m map[int]int
}

//go:noescape
func useT40(*T40)

func newT40() *T40 {
	ret := T40{}
	ret.m = make(map[int]int, 42) // ERROR "live at call to makemap: &ret$"
	return &ret
}

func bad40() {
	t := newT40()
	_ = t
	printnl()
}

func good40() {
	ret := T40{}              // ERROR "stack object ret T40$"

type T40 struct {
	m map[int]int
}

//go:noescape
func useT40(*T40)

func newT40() *T40 {
	ret := T40{}
	ret.m = make(map[int]int, 42) // ERROR "live at call to makemap: &ret$"
	return &ret
}

func bad40() {
	t := newT40()
	_ = t
	printnl()
}

func good40() {
	ret := T40{}              // ERROR "stack object ret T40$"

	}
	//fmt.Printf("% x %q\n", b, b)
	//Debug(b)
	dec := NewDecoder(b)
	newEt0 := new(ET0)
	err = dec.Decode(newEt0)
	if err != nil {
		t.Fatal("error decoding ET0:", err)
	}

	if !reflect.DeepEqual(et0, newEt0) {
		t.Fatalf("invalid data for et0: expected %+v; got %+v", *et0, *newEt0)
	}
	if b.Len() != 0 {
		t.Error("not at eof;", b.Len(), "bytes left")
	}
	//	t.FailNow()

    EXPECT_LT(max_error, 1e-3f);
  }

  float RV(const std::vector<float>& v) {
    return v[random::New64() % v.size()];
  }

  complex64 CRV(const std::vector<complex64>& v) {
    return v[random::New64() % v.size()];
  }

  complex64 conjugate(const complex64& val) {
    return complex64(val.real(), -val.imag());
  }

  Scope scope_;
};

		t.Errorf("invalid data after update, got %+v", product)
	}

	// Code changed, price should changed
	DB.Model(&product).Select("Name", "Code", "Price").Updates(Product3{Name: "Product New4", Code: ""})
	if product.Name != "Product New4" || product.Price != 320 || product.Code != "" {
		t.Errorf("invalid data after update, got %+v", product)
	}

	DB.Model(&product).UpdateColumns(Product3{Code: "L1215"})

      tensor_flat(i) = cur;
      cur += 5e-2;
    }
    // Fischer-Yates shuffle the array
    for (size_t i = tensor->NumElements() - 1; i >= 1; i--) {
      // j <- random integer 0 <= j <= i
      size_t j = random::New64() % (i + 1);
      // swap values at i, j
      T tmp = tensor_flat(i);
      tensor_flat(i) = tensor_flat(j);
      tensor_flat(j) = tmp;
    }
  }

  Scope scope_;
};

constexpr char kDefaultShardingValue[] = "";
constexpr char kMirroredVariableIndicesAttr[] = "_mirrored_variable_indices";

std::string GetRandomStateVariableName() {
  return absl::StrCat("VariablesFormatState_", tensorflow::random::New64());
}

#define GEN_PASS_DEF_TPUVARIABLERUNTIMEREFORMATTINGPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/host_runtime/runtime_passes.h.inc"

struct TPUVariableRuntimeReformattingPass