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            "type": "string"
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        "type": "object"
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      "io.k8s.apimachinery.pkg.apis.meta.v1.OwnerReference": {

embedding_indices and aggregation_weights into rows.
It corresponds to ids.row_splits in embedding_lookup(), when ids is a
RaggedTensor.}]>:$sample_splits,
    Arg<Variadic<TF_I32OrI64Tensor>, [{A list of rank 1 Tensors, indices into the embedding tables.
It corresponds to ids.values in embedding_lookup(), when ids is a RaggedTensor.}]>:$embedding_indices,

message LinuxContainerUser {
  // UID is the primary uid initially attached to the first process in the container
  optional int64 uid = 1;

  // GID is the primary gid initially attached to the first process in the container
  optional int64 gid = 2;

  // SupplementalGroups are the supplemental groups initially attached to the first process in the container
  // +optional
  // +listType=atomic

    *   Benchmark for tf.scan in graph and eager modes.
    *   Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D.
    *   Making ids unique in `nn.embedding_lookup_sparse`. This helps to reduce
        RPC calls for looking up the embeddings when there are repeated ids in
        the batch.
    *   Support indicator column in boosted trees.
    *   Prevent `tf.gradients()` from backpropagating through integer tensors.