}

def TF_ConcatOp : TF_Op<"Concat", [Pure]> {
  let summary = "Concatenates tensors along one dimension.";

  let arguments = (ins
    Arg<TF_Int32Tensor, [{0-D.  The dimension along which to concatenate.  Must be in the
range [0, rank(values)).}]>:$concat_dim,
    Arg<Variadic<TF_Tensor>, [{The `N` Tensors to concatenate. Their ranks and types must match,

*   Add a `tf.distribute.cluster_resolver.TPUClusterResolver.connect` API to
    simplify TPU initialization.
*   Add `tf.distribute.Strategy.gather` and
    `tf.distribute.ReplicaContext.all_gather` methods to gather and concatenate
    `tf.distribute.DistributedValues` across workers and devices.

### `tf.keras`:

*   Introduces experimental preprocessing layers API

the owner of the PVC, i.e. the PVC will be deleted together with the pod.  The name of the PVC will be `<pod name>-<volume name>` where `<volume name>` is the name from the `PodSpec.Volumes` array entry. Pod validation will reject the pod if the concatenated name is not valid for a PVC (for example, too long).\n\nAn existing PVC with that name that is not owned by the pod will *not* be used for the pod to avoid using an unrelated volume by mistake. Starting the pod is then blocked until the unrelated...