*   The new backend is used for 8 bits full integer post-training
            quantization
        *   The new backend removes the redundant rescales and fixes some bugs
            (shared weight/bias, extremely small scales, etc)
        *   Set `experimental_new_quantizer` in tf.lite.TFLiteConverter to False
            to disable this change
*   `tf.keras`

            "type": "string"
          },
          "kind": {
            "description": "kind expected values are Shared: multiple blob disks per storage account  Dedicated: single blob disk per storage account  Managed: azure managed data disk (only in managed availability set). defaults to shared",
            "type": "string"
          },
          "readOnly": {

					DNSPolicy:     core.DNSClusterFirst,
				},
			},
		},
		"Valid: request more than one hugepages size": {
			pod: &core.Pod{
				ObjectMeta: metav1.ObjectMeta{Name: "hugepages-shared", Namespace: "ns"},
				Spec: core.PodSpec{
					Containers: []core.Container{{
						Name: "ctr", Image: "image", ImagePullPolicy: "IfNotPresent", TerminationMessagePolicy: "File",

list. If the deletionTimestamp of the object is non-nil, entries in this list can only be removed. Finalizers may be processed and removed in any order.  Order is NOT enforced because it introduces significant risk of stuck finalizers. finalizers is a shared field, any actor with permission can reorder it. If the finalizer list is processed in order, then this can lead to a situation in which the component responsible for the first finalizer in the list is waiting for a signal (field value, external system,...

data: the value of the reduction across all `num_devices` devices.
reduction: the reduction operation to perform.
num_devices: The number of devices participating in this reduction.
shared_name: Identifier that shared between ops of the same reduction.
  }];

  let arguments = (ins
    TensorOf<[TF_Float16, TF_Float32, TF_Float64, TF_Int32, TF_Int64]>:$input,