type T [4000]byte
	m := map[int]T{}
	x := m[0]
	if x != (T{}) {
		t.Errorf("map value not zero")
	}
	y, ok := m[0]
	if ok {
		t.Errorf("map value should be missing")
	}
	if y != (T{}) {
		t.Errorf("map value not zero")
	}
}

type empty struct {
}

func TestEmptyKeyAndValue(t *testing.T) {
	a := make(map[int]empty, 4)
	b := make(map[empty]int, 4)

  }];
}

def MapFnOp : TensorflowMlrt_Op<"map_fn", [AttrSizedOperandSegments, Pure]> {
  let summary = "The Parallel Map for tf_mlrt dialect";
  let description = [{
    The Pmap executes body function in parallel for all ranges up to $max_iterations.

    The pseudo code:
      for(int i = 0; i < $max_iterations; i++) {

	}

	found := findTypes(t, dwarf, want)
	if len(found) != len(want) {
		t.Errorf("found %v, want %v", found, want)
	}
}

func findTypes(t *testing.T, dw *dwarf.Data, want map[string]bool) (found map[string]bool) {
	found = make(map[string]bool)
	rdr := dw.Reader()
	for entry, err := rdr.Next(); entry != nil; entry, err = rdr.Next() {
		if err != nil {
			t.Fatalf("error reading DWARF: %v", err)
		}
		switch entry.Tag {

	REG_VS60
	REG_VS61
	REG_VS62
	REG_VS63

	REG_CR0
	REG_CR1
	REG_CR2
	REG_CR3
	REG_CR4
	REG_CR5
	REG_CR6
	REG_CR7

	// MMA accumulator registers, these shadow VSR 0-31
	// e.g MMAx shadows VSRx*4-VSRx*4+3 or
	//     MMA0 shadows VSR0-VSR3
	REG_A0
	REG_A1
	REG_A2
	REG_A3
	REG_A4
	REG_A5
	REG_A6
	REG_A7

	REG_MSR
	REG_FPSCR
	REG_CR

			}

			parser := expfmt.TextParser{}
			mfMap, err := parser.TextToMetricFamilies(strings.NewReader(rec.Body.String()))
			if err != nil && !tt.expectParseError {
				t.Fatalf("failed to parse metrics: %v", err)
			} else if err == nil && tt.expectParseError {
				t.Fatalf("expected a prse error, got %+v", mfMap)
			}
		})
	}
}

func TestNegotiateMetricsFormat(t *testing.T) {