s:\n\n- 1.5 will be serialized as \"1500m\" - 1.5Gi will be serialized as \"1536Mi\"\n\nNote that the quantity will NEVER be internally represented by a floating point number. That is the whole point of this exercise.\n\nNon-canonical values will still parse as long as they are well formed, but will be re-emitted in their canonical form. (So always use canonical form, or don't diff.)\n\nThis format is intended to make it difficult to use these numbers without writing some sort of special handling...

          "unavailableReplicas": {
            "description": "Total number of unavailable pods targeted by this deployment. This is the total number of pods that are still required for the deployment to have 100% available capacity. They may either be pods that are running but not yet available or pods that still have not been created.",
            "format": "int32",
            "type": "integer"
          },
          "updatedReplicas": {

# Release 2.14.0

## Tensorflow

### Breaking Changes

*   Support for Python 3.8 has been removed starting with TF 2.14. The TensorFlow 2.13.1 patch release will still have Python 3.8 support.

*  `tf.Tensor`

                    Note that a parent is also an ancestor, so for objects where the parent is the
                    relevant object for status, this struct SHOULD still be used.


                    This struct is intended to be used in a slice that's effectively a map,
                    with a composite key made up of the AncestorRef and the ControllerName.

Traditionally, the Kubernetes scheduler was based on the assumption that additional persistent storage is available everywhere in the cluster and has infinite capacity. Topology constraints addressed the first point, but up to now pod scheduling was still done without considering that the remaining storage capacity may not be enough to start a new pod. [Storage capacity tracking](https://github.com/kubernetes/enhancements/tree/master/keps/sig-storage/1472-storage-capacity-tracking), a new alpha feature,...

  let summary = [{
Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same shape and type.
  }];

  let description = [{
Quantization is called fake since the output is still in floating point.
  The API converts inputs into values within the range [min and max] and returns
  as output.

Attributes

*   `[min; max]` define the clamping range for the `inputs` data.

	}, {
		testName: "RunAsUserName's DNS Domain is too long",
		windowsOptions: &core.WindowsSecurityContextOptions{
			// even if this tests the max Domain length, the Domain should still be "valid".
			RunAsUserName: toPtr(strings.Repeat(strings.Repeat("a", 63)+".", 4)[:253] + ".com\\user"),
		},
		expectedErrorSubstring: "runAsUserName's Domain length must be under",
	}, {