lost - No fractional digits will be emitted - The exponent (or suffix) is as large as possible.\n\nThe sign will be omitted unless the number is negative.\n\nExamples:\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....

lost - No fractional digits will be emitted - The exponent (or suffix) is as large as possible.\n\nThe sign will be omitted unless the number is negative.\n\nExamples:\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....

                            instead interpret the zero duration as the longest possible value to which
                            the timeout can be set.


                            This timeout is intended to cover as close to the whole request-response transaction
                            as possible although an implementation MAY choose to start the timeout after the entire

padded on the left with ones. So this gives the same result as the previous
example:

>>> x = tf.constant([1, 2, 3])   # Shape (3,)
>>> y = tf.broadcast_to(x, [2, 3])


When doing broadcasted operations such as multiplying a tensor
by a scalar, broadcasting (usually) confers some time or space
benefit, as the broadcasted tensor is never materialized.

    issue as quickly as possible.
    *   This version removes the `tensorrt` Python package dependencies from the
        tensorflow[and-cuda] installation method to ensure `pip install
        tensorflow[and-cuda]` works as originally intended for TensorFlow 2.15.
    *   Support for TensorRT is otherwise unaffected as long as TensorRT is
        already installed on the system.

                              format: int32
                              type: integer
                            minHealthPercent:
                              description: Outlier detection will be enabled as long
                                as the associated load balancing pool has at least
                                min_health_percent hosts in healthy mode.
                              format: int32
                              type: integer

                              format: int32
                              type: integer
                            minHealthPercent:
                              description: Outlier detection will be enabled as long
                                as the associated load balancing pool has at least
                                min_health_percent hosts in healthy mode.
                              format: int32
                              type: integer

			isExpectedFailure: true, // updating secretRef will fail as the object is immutable eventhough the secretRef is valid
			oldVolume:         validCSIVolume,
			newVolume:         getCSIVolumeWithSecret(validCSIVolume, shortSecretRef, "controllerPublish"),
		},
		"csi-cntrlpublish-enabled-with-longSecretRef": {
			isExpectedFailure: true, // updating secretRef will fail as the object is immutable eventhough the secretRef is valid