Res<TF_Float32Tensor, [{A 1D Tensor for the computed batch mean, to be used by TensorFlow
to compute the running mean.}]>:$batch_mean,
    Res<TF_Float32Tensor, [{A 1D Tensor for the computed batch variance, to be used by
TensorFlow to compute the running variance.}]>:$batch_variance,
    Res<TF_Float32Tensor, [{A 1D Tensor for the computed batch mean, to be reused

            "format": "int32",
            "type": "integer"
          },
          "numberUnavailable": {
            "description": "The number of nodes that should be running the daemon pod and have none of the daemon pod running and available (ready for at least spec.minReadySeconds)",

                          items:
                            type: string
                          type: array
                        credentialName:
                          description: For gateways running on Kubernetes, the name
                            of the secret that holds the TLS certs including the CA
                            certificates.
                          type: string

                          items:
                            type: string
                          type: array
                        credentialName:
                          description: For gateways running on Kubernetes, the name
                            of the secret that holds the TLS certs including the CA
                            certificates.
                          type: string

                          items:
                            type: string
                          type: array
                        credentialName:
                          description: For gateways running on Kubernetes, the name
                            of the secret that holds the TLS certs including the CA
                            certificates.
                          type: string

        `tf.keras.utils.image_dataset_from_directory`,`tf.keras.utils.text_dataset_from_directory`,
        and `audio_dataset_from_directory`, to be used with the
        `validation_split` argument, for returning both dataset splits at once,
        as a tuple.
    *   Added `tf.keras.utils.split_dataset` utility to split a `Dataset` object
        or a list/tuple of arrays into two `Dataset` objects (e.g. train/test).

                        underlying implementation must actively reject request attempts to this
                        backend, by rejecting the connection or returning a 500 status code.
                        Request rejections must respect weight; if an invalid backend is
                        requested to have 80% of requests, then 80% of requests must be rejected