effective_hidden_scale_intermediate = tensor<!quant.calibrated<f32<-5.000000e-01:5.000000e-01>>>,
      fused_activation_function = "TANH",
      input_to_cell_intermediate = tensor<!quant.calibrated<f32<-4.000000e+00:4.000000e+00>>>,
      input_to_forget_intermediate = tensor<!quant.calibrated<f32<-1.600000e+01:1.600000e+01>>>,
      input_to_output_intermediate = tensor<!quant.calibrated<f32<-1.000000e+00:1.000000e+00>>>,

  %cst = "tf.Const"() {device = "/job:localhost/replica:0/task:0/device:CPU:0", value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00, 7.000000e+00, 8.000000e+00, 9.000000e+00]> : tensor<9xf32>} : () -> tensor<9xf32>

        x=ops.convert_to_tensor(input_data)
    )
    # Tests that the quantized graph outputs similar values. The rtol and atol
    # values are arbitrary.
    self.assertAllClose(new_outputs, expected_outputs, rtol=0.02, atol=0.04)

    # Due to other meta data, the compression is not exactly 1/4.
    self.assertLess(
        testing.get_size_ratio(
            self._output_saved_model_path, self._input_saved_model_path
        ),

var fmod = []float64{
	4.197615023265299782906368e-02,
	2.261127525421895434476482e+00,
	3.231794108794261433104108e-02,
	4.989396381728925078391512e+00,
	3.637062928015826201999516e-01,
	1.220868282268106064236690e+00,
	4.770916568540693347699744e+00,
	1.816180268691969246219742e+00,
	8.734595415957246977711748e-01,
	1.314075231424398637614104e+00,
}

type fi struct {
	f float64
	i int
}

var frexp = []fi{

                               slice[0:0:0] no panic
                               slice[0:0:3] no panic
                               slice[0:0:4] runtime error: slice bounds out of range [::4] with capacity 3
                      slice[0:0:9876543210] runtime error: slice bounds out of range [::9876543210] with capacity 3

		{name: "object not found", volID: "vol-non-existing", attachID: getAttachmentName("vol-003", testDriver, nodeName)},
		{
			name:       "API error",
			volID:      "vol-004",
			attachID:   getAttachmentName("vol-004", testDriver, nodeName),
			shouldFail: true, // All other API errors should be propagated to caller
			reactor: func(action core.Action) (handled bool, ret runtime.Object, err error) {

	// Reuse the data structure above, now testing "unscaling".
	// Now, we test that no scaling happens if we are in a very close margin to the tolerance
	target = math.Abs(1/(requestedToUsed*(1-defaultTestingTolerance))) + .004
	finalCPUPercentTarget = int32(target * 100)
	tc.resource.targetUtilization = finalCPUPercentTarget
	tc.currentReplicas = startPods
	tc.expectedReplicas = startPods
	tc.runTest(t)
}

	//   [    4] section_0001      PROGBITS        00000000 000034 000004 00  WA  0   0  4
	//   [    5] section_0002      PROGBITS        00000000 000038 000004 00  WA  0   0  4
	//   [ section_0003 ~ section_ff06 truncated ]
	//   [65290] section_ff07      PROGBITS        00000000 03fc4c 000004 00  WA  0   0  4
	//   [65291] section_ff08      PROGBITS        00000000 03fc50 000004 00  WA  0   0  4