//fmt.Printf("testing nsamples=%v mean=%v stddev=%v seed=%v\n", nsamples, mean, stddev, seed);

	samples := generateNormalSamples(nsamples, mean, stddev, seed)
	errorScale := max(1.0, stddev) // Error scales with stddev
	expected := &statsResults{mean, stddev, 0.10 * errorScale, 0.08 * errorScale}

	// Make sure that the entire set matches the expected distribution.
	checkSampleDistribution(t, samples, expected)

// CHECK-LABEL: func @uniform_quantize_cast_dequantize
func.func @uniform_quantize_cast_dequantize(%arg0: tensor<1xf32>) -> tensor<1xf32>
{
  %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>
  %scales1 = "tf.Const"() { value = dense<3.0> : tensor<f32> } : () -> tensor<f32>
  %zps1 = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32>

        auto shape = GetAsVector(quant_tensor->shape());
        if (kUseUpdatedHybridSchemeDefault) {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), shape[0]);
        } else {
          EXPECT_EQ(quant_tensor->quantization()->scale()->size(), 1);
        }
      } else {
        EXPECT_EQ(quant_tensor->type(), TensorType_FLOAT32);
      }
    }

						return Quantity{i: int64Amount{value: result, scale: Scale(scale)}, Format: format, s: str}, nil
					}
				default:
					if scale%3 == 0 && !strings.HasSuffix(shifted, "000") && shifted[0] != '0' {
						return Quantity{i: int64Amount{value: result, scale: Scale(scale)}, Format: format, s: str}, nil
					}
				}
				return Quantity{i: int64Amount{value: result, scale: Scale(scale)}, Format: format}, nil
			}
		}
	}

	}

	normScale := make([]float64, len(baseVals))
	for i := range baseVals {
		if srcVals[i] == 0 {
			normScale[i] = 0.0
		} else {
			normScale[i] = float64(baseVals[i]) / float64(srcVals[i])
		}
	}
	p.ScaleN(normScale)
	return nil
}

func isZeroSample(s *Sample) bool {
	for _, v := range s.Value {
		if v != 0 {
			return false
		}
	}
	return true
}

type profileMerger struct {

}

TFE_MonitoringBuckets* TFE_MonitoringNewExponentialBuckets(double scale,
                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,

    attributes {tf_quant.quantized_ops = ${quantized_ops}} {
      // Given the convolution takes 2 qint8 inputs and output a qint32.
      // The accumulation scale is (input_scale * filter_scale).
      // The accumulation zero point is 0.
      %accum_scale = "tf.Mul"(%input_scale, %filter_scale) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

			return false
		}
	case policy.WhenScaled == delete && policy.WhenDeleted == delete:
		podScaledDown := !podInOrdinalRange(pod, set)
		// If a pod is scaled down, there should be no set ref and a pod ref;
		// if the pod is not scaled down it's the other way around.
		if podScaledDown == hasOwnerRef(claim, set) {
			return false
		}
		if podScaledDown != hasOwnerRef(claim, pod) {
			return false
		}

	"math/bits"
	mathrand "math/rand"
	"reflect"
	"testing"
	"testing/quick"
)

func (v Element) String() string {
	return hex.EncodeToString(v.Bytes())
}

// quickCheckConfig returns a quick.Config that scales the max count by the
// given factor if the -short flag is not set.
func quickCheckConfig(slowScale int) *quick.Config {
	cfg := new(quick.Config)
	if !testing.Short() {
		cfg.MaxCountScale = float64(slowScale)
	}

   `scale` and `zero point`. Currently, the allowed activations are
   `NONE`, `RELU`, `RELU6` and `RELU_N1_TO_1`.

    Example:

    ```mlir
    %3, %4 = tfr.quant_act_range(%2, %1, %0) :
        (tfr.attr, float, i64) -> (tfr.tensor, tfr.tensor)
    ```
  }];

  let arguments = (ins
      TFR_AttrType:$act,
      F32:$scale,
      I64:$zp);