return failure();
  }

  return success();
}

// Matches the inverse scales operand for the uniform_quantize and
// uniform_dequantize functions. Returns `failure()` if it doesn't match.
LogicalResult MatchInverseScalesOperand(Value inverse_scales) {
  if (!inverse_scales) {
    LLVM_DEBUG(llvm::dbgs() << "Inverse scales value is empty.\n");
    return failure();
  }

  auto inverse_scales_type =

  // Consuming op should already know about Quantized channel information,
  // so not passing it during conversion. This design might change if needed.
  ArrayRef<double> scales = qtype.getScales();
  ArrayRef<int64_t> zero_points = qtype.getZeroPoints();
  const int num_channels = scales.size();
  TensorType scale_type = RankedTensorType::get(
      {static_cast<int64_t>(num_channels)}, rewriter.getF32Type());

        "name": "coordination.k8s.io"
      },
      "versions": [
        {
          "freshness": "Current",
          "resources": [
            {
              "resource": "leases",
              "responseKind": {
                "group": "",
                "kind": "Lease",
                "version": ""
              },
              "scope": "Namespaced",

        "name": "coordination.k8s.io"
      },
      "versions": [
        {
          "freshness": "Current",
          "resources": [
            {
              "resource": "leases",
              "responseKind": {
                "group": "",
                "kind": "Lease",
                "version": ""
              },
              "scope": "Namespaced",

// CHECK-NEXT:  %[[cst:.*]] = arith.constant dense<-2.540000e+01> : tensor<f32>
// CHECK-NEXT:  "tfl.quantize"(%[[cst]]) <{qtype = tensor<!quant.uniform<u8:f32, 0.099607841641295186:255>>}> {volatile}
}

// Make sure biases are not shared.
// CHECK-LABEL: QuantizeSharedBiases
func.func @QuantizeSharedBiases(
    %arg0: tensor<1x224x224x3x!quant.uniform<u8:f32, 1.0>>,
    %arg1: tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 1.0>>,

				scheduledRunningReadyPodOnNode1,
				scheduledRunningNotReadyPod,
			},
			diff:                 1,
			expectedPodsToDelete: []*v1.Pod{scheduledRunningNotReadyPod},
		},
		{
			name: "various pod phases and conditions, diff = len(pods)",
			pods: []*v1.Pod{
				scheduledRunningReadyPodOnNode1,
				scheduledRunningReadyPodOnNode1,
				scheduledRunningReadyPodOnNode2,
				scheduledRunningNotReadyPod,

//    accomplished using STW.
//
//    b. Start the world. From this point, GC work is done by mark
//    workers started by the scheduler and by assists performed as
//    part of allocation. The write barrier shades both the
//    overwritten pointer and the new pointer value for any pointer
//    writes (see mbarrier.go for details). Newly allocated objects
//    are immediately marked black.
//

            tensor_spec.TensorSpec(
                shape=input_shape, dtype=dtypes.float32, name='input_tensor'
            )
        ),
    )
    return model

  # Prepares sample einsum input data shapes.
  # This function returns:
  # 1. Shape for input 1
  # 2. Shape for input 2
  # 3. Shape for bias
  # 4. Signature for input 1 (Could contain None dimension)

		}
	}
	// Always update the probe time if node lease is renewed.
	// Note: If kubelet never posted the node status, but continues renewing the
	// heartbeat leases, the node controller will assume the node is healthy and
	// take no action.
	observedLease, _ := nc.leaseLister.Leases(v1.NamespaceNodeLease).Get(node.Name)
	if observedLease != nil && (savedLease == nil || savedLease.Spec.RenewTime.Before(observedLease.Spec.RenewTime)) {

		}
		rescale = desiredReplicas != currentReplicas
	}

	if rescale {
		scale.Spec.Replicas = desiredReplicas
		_, err = a.scaleNamespacer.Scales(hpa.Namespace).Update(ctx, targetGR, scale, metav1.UpdateOptions{})
		if err != nil {