"'h_scale' type: 'float'} attr : { name: 'max_classes_per_detection' "
    "type: 'int'} attr : { name: 'max_detections' type: 'int'} attr : { "
    "name: 'nms_iou_threshold' type: 'float'} attr : { name: "
    "'nms_score_threshold' type: 'float'} attr : { name: 'num_classes' type: "
    "'int'} attr : { name: 'w_scale' type: 'float'} attr : { name: 'x_scale' "

// CHECK-DAG: %[[w0_quantized:.*]] = "tf.Const"() <{value = dense<{{\[\[\[\[}}-42, 18
// CHECK-DAG: %[[in_scale:.*]] = "tf.Const"() <{value = dense<0.0039215642> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[in_out_zp:.*]] = "tf.Const"() <{value = dense<-128> : tensor<i32>}> : () -> tensor<i32>
// CHECK-DAG: %[[w0_scale:.*]] = "tf.Const"() <{value = dense<0.0150353173> : tensor<f32>}> : () -> tensor<f32>

    for (RequantizeState& rescale : rescales) {
      if (rescale.params == quantized_type) {
        rescale.users.emplace_back(op, operand_index);
        return true;
      }
    }
    RequantizeState& rescale = rescales.emplace_back();
    rescale.pos = RequantizeState::ON_OUTPUT;
    rescale.params = quantized_type;
    rescale.users.emplace_back(op, operand_index);
    return true;
  }

	}
	return q.i.Sign()
}

// AsScale returns the current value, rounded up to the provided scale, and returns
// false if the scale resulted in a loss of precision.
func (q *Quantity) AsScale(scale Scale) (CanonicalValue, bool) {
	if q.d.Dec != nil {
		return q.d.AsScale(scale)
	}
	return q.i.AsScale(scale)
}

// RoundUp updates the quantity to the provided scale, ensuring that the value is at

  auto &state = GetResultQuantState(op, res_index);
  if (state.params == params) {
    return false;
  }
  if (!state.IsEmpty()) {
    auto &rescale = GetResultRequantizeState(op, res_index);
    rescale.params = params;
    rescale.pos = RequantizeState::ON_INPUT;
    return false;
  }
  state.params = params;
  return true;
}

    if (multiplier_attr.isSplat()) {
      double new_scale = multiplier_array.front() * uniform_type.getScale();
      new_qtype = UniformQuantizedType::get(
          uniform_type.getFlags(), uniform_type.getStorageType(),
          uniform_type.getExpressedType(), new_scale,
          uniform_type.getZeroPoint(), uniform_type.getStorageTypeMin(),

      e.preventDefault();
    } else if (mode == TOUCHZOOM) {
      // Get two touches; new gap; rescale to ratio.
      const t1 = findTouch(e.touches, touchid);
      const t2 = findTouch(e.touches, touchid2);
      if (t1 == null || t2 == null) return;
      const gap = touchGap(t1, t2);
      rescale(initScale * gap / initGap, centerPoint);
      e.preventDefault();
    }
  }

		t.Fatalf("error updating scale %v: %v", update, err)
	}
	obj, err := storage.Scale.Get(ctx, name, &metav1.GetOptions{})
	if err != nil {
		t.Fatalf("error fetching scale for %s: %v", name, err)
	}
	scale := obj.(*autoscaling.Scale)
	if scale.Spec.Replicas != replicas {
		t.Errorf("wrong replicas count expected: %d got: %d", replicas, rc.Spec.Replicas)
	}

	update.ResourceVersion = rc.ResourceVersion

// Package-wide variables from generator "generated".
option go_package = "k8s.io/api/autoscaling/v2";

// ContainerResourceMetricSource indicates how to scale on a resource metric known to
// Kubernetes, as specified in requests and limits, describing each pod in the
// current scale target (e.g. CPU or memory).  The values will be averaged
// together before being compared to the target.  Such metrics are built in to

namespace mlir::quant::stablehlo {

// Checks whether an op is connected with a quantized composite function. If
// not, the same-scale op will not be quantized. This decision is based on the
// current assumption that the performance gain of the same-scale op itself
// could not beat the overhead of the quantize and dequantize routines need to
// be added around that op. When the assumption changes, this policy might