// CHECK:             mhlo.return %[[SUM2]] : tensor<f32>
// CHECK:           })
// CHECK-SAME:        -> tensor<2x4x6x7xf32>
// CHECK:           %[[RESULT:.*]] = mhlo.divide %[[DIVIDEND]], %[[DIVISOR]] : tensor<2x4x6x7xf32>
// CHECK:           return %[[RESULT]] : tensor<2x4x6x7xf32>
// CHECK:         }
func.func @avgpool_same_padding(%arg0: tensor<2x12x21x7xf32>) -> tensor<2x4x6x7xf32> {

  %0 = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>}> : (tensor<1x1xf32>) -> tensor<1x1000xf32>
  %1 = mhlo.divide %0, %arg1 : tensor<1x1000xf32>
  %2 = mhlo.divide %arg1, %0 : tensor<1x1000xf32>
  func.return %1, %2 : tensor<1x1000xf32>, tensor<1x1000xf32>
}

// CHECK-LABEL:   func @broadcast_div_chlo(

    } else {
      // For mean, add replicas in the same group. Then divide the sum by the
      // number of replicas in each group below.
      assert(reduce_op == "Mean");
      BuildReduceBody<AddOp>(element_type, &reduce_scatter.getComputation(),
                             &rewriter);
    }
    Value result = reduce_scatter.getResult();

    // For mean, divide the merge result by group size.
    if (reduce_op == "Mean") {

var(--tw-divide-y-reverse)))`,"border-bottom-width":`calc(${r} * var(--tw-divide-y-reverse))`}})},{values:t("divideWidth"),type:["line-width","length","any"]}),e({".divide-y-reverse > :not([hidden]) ~ :not([hidden])":{"@defaults border-width":{},"--tw-divide-y-reverse":"1"},".divide-x-reverse > :not([hidden]) ~ :not([hidden])":{"@defaults border-width":{},"--tw-divide-x-reverse":"1"}})},divideStyle:({addUtilities:i})=>{i({".divide-solid > :not([hidden]) ~ :not([hidden])":{"border-style":"solid"},".divide-dashed...

		}
		return true, false
	}
	return false, false
}

func mountNameAlreadyExists(name string, devices map[string]string) bool {
	if _, ok := devices[name]; ok {
		return true
	}
	return false
}

func mountPathAlreadyExists(mountPath string, devices map[string]string) bool {
	for _, devPath := range devices {
		if mountPath == devPath {
			return true
		}
	}

	return false
}

	"maxLimitRequestRatio": "MaxLimitRequestRatio if specified, the named resource must have a request and limit that are both non-zero where limit divided by request is less than or equal to the enumerated value; this represents the max burst for the named resource.",
}

func (LimitRangeItem) SwaggerDoc() map[string]string {
	return map_LimitRangeItem
}

type PersistentVolumeClaimList struct {
	metav1.TypeMeta
	// +optional
	metav1.ListMeta
	Items []PersistentVolumeClaim
}

// PersistentVolumeClaimSpec describes the common attributes of storage devices
// and allows a Source for provider-specific attributes
type PersistentVolumeClaimSpec struct {
	// Contains the types of access modes required
	// +optional
	AccessModes []PersistentVolumeAccessMode

	// MaxLimitRequestRatio if specified, the named resource must have a request and limit that are both non-zero where limit divided by request is less than or equal to the enumerated value; this represents the max burst for the named resource.
	// +optional

  // +optional
  map<string, .k8s.io.apimachinery.pkg.api.resource.Quantity> defaultRequest = 5;

  // MaxLimitRequestRatio if specified, the named resource must have a request and limit that are both non-zero where limit divided by request is less than or equal to the enumerated value; this represents the max burst for the named resource.
  // +optional
  map<string, .k8s.io.apimachinery.pkg.api.resource.Quantity> maxLimitRequestRatio = 6;
}

   The `read_vs_write_current_requests` metric histogram is adjusted, in the case of using API Priority and Fairness instead of max-in-flight, to divide by the relevant limit: sum of queue capacities for waiting requests, sum of seat limits for executing requests. ([#110164](https://github.com/kubernetes/kubernetes/pull/110164), [@MikeSpreitzer](https://github.com/MikeSpreitzer))