}
}

func TestSecondsToUTCAndBack(t *testing.T) {
	f := func(sec int64) bool { return Unix(sec, 0).UTC().Unix() == sec }
	f32 := func(sec int32) bool { return f(int64(sec)) }
	cfg := &quick.Config{MaxCount: 10000}

	// Try a reasonable date first, then the huge ones.
	if err := quick.Check(f32, cfg); err != nil {
		t.Fatal(err)
	}
	if err := quick.Check(f, cfg); err != nil {
		t.Fatal(err)
	}
}

include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td"
include "tensorflow/compiler/mlir/lite/utils/utils.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"

// Checks if the param passed is a F32 ElementsAttr.
def F32ElementsAttr : ElementsAttrBase<
  CPred<"$_self.isa<ElementsAttr>() && $_self.cast<ElementsAttr>().getShapedType().getElementType().isF32()">,
        "32 bit float constant tensor">;

			t.Errorf("FormatAndParse %d: %q(%d) %q(%d)", sec, t1, t1.Unix(), t2, t2.Unix())
			return false
		}
		return true
	}
	f32 := func(sec int32) bool { return f(int64(sec)) }
	cfg := &quick.Config{MaxCount: 10000}

	// Try a reasonable date first, then the huge ones.
	if err := quick.Check(f32, cfg); err != nil {
		t.Fatal(err)
	}
	if err := quick.Check(f, cfg); err != nil {
		t.Fatal(err)
	}
}

    )
    module_str = self._extract_first_xla_call_module_op(
        self._output_saved_model_path
    )

    # Check add is not quantized.
    self.assertTrue(re.search(r'stablehlo.add.*f32>', module_str))

  @parameterized.parameters(
      testing.parameter_combinations([{
          'shape_dynamic': (
              False,
              True,
          ),
      }])
  )

		ctxt.hash[name] = s
		if init != nil {
			init(s)
		}
	}
	ctxt.hashmu.Unlock()
	return s
}

func (ctxt *Link) Float32Sym(f float32) *LSym {
	i := math.Float32bits(f)
	name := fmt.Sprintf("$f32.%08x", i)
	return ctxt.LookupInit(name, func(s *LSym) {
		s.Size = 4
		s.WriteFloat32(ctxt, 0, f)
		s.Type = objabi.SRODATA
		s.Set(AttrLocal, true)
		s.Set(AttrContentAddressable, true)

//   %4 = mhlo.constant dense<0.000000e+00> : tensor<f32>
//   %5 = "mhlo.broadcast"(%4)
//   %6 = "mhlo.select"(%3, %input, %5)
//   %7 = "mhlo.reduce"(%6, %4) ({
//   ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
//     %9 = mhlo.add %arg1, %arg2 : tensor<f32>
//     "mhlo.return"(%9) : (tensor<f32>) -> ()
//   }) {dimensions = dense<0> : tensor<1xi64>}
//

	}

	// Rewrite float constants to values stored in memory.
	switch p.As {
	case AFMOVS:
		if p.From.Type == obj.TYPE_FCONST && !convertFMOVtoXXSPLTIDP(p) {
			f32 := float32(p.From.Val.(float64))
			p.From.Type = obj.TYPE_MEM
			p.From.Sym = ctxt.Float32Sym(f32)
			p.From.Name = obj.NAME_EXTERN
			p.From.Offset = 0
		}

	case AFMOVD:
		if p.From.Type == obj.TYPE_FCONST {
			f64 := p.From.Val.(float64)

          sess, tags=tags, export_dir=self._output_saved_model_path
      )

      # The graph should contain a quantized function call (it contains a
      # single f32 matmul node).
      self.assertTrue(
          self._contains_quantized_function_call(
              output_meta_graph_def.graph_def
          )
      )
      self.assertCountEqual(

// issue 22958

typedef struct {
	unsigned long long f8  : 8;
	unsigned long long f16 : 16;
	unsigned long long f24 : 24;
	unsigned long long f32 : 32;
	unsigned long long f40 : 40;
	unsigned long long f48 : 48;
	unsigned long long f56 : 56;
	unsigned long long f64 : 64;
} issue22958Type;

// issue 23356
int a(void) { return 5; };