"type": "string"
          },
          "ownerReferences": {

dataSource and dataSourceRef: * While dataSource only allows two specific types of objects, dataSourceRef\n  allows any non-core object, as well as PersistentVolumeClaim objects.\n* While dataSource ignores disallowed values (dropping them), dataSourceRef\n  preserves all values, and generates an error if a disallowed value is\n  specified.\n* While dataSource only allows local objects, dataSourceRef allows objects\n  in any namespaces.\n(Beta) Using this field requires the AnyVolumeDataSource feature...

                    In this case, the relevant object for status is the Gateway, and that is the
                    ancestor object referred to in this status.


                    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.

                    - name
                    x-kubernetes-list-type: map
                type: object
            required:
            - url
            type: object
          status:
            type: object
            x-kubernetes-preserve-unknown-fields: true
        required:
        - spec
        type: object
    served: true
    storage: true
    subresources:
      status: {}
---

                    - name
                    x-kubernetes-list-type: map
                type: object
            required:
            - url
            type: object
          status:
            type: object
            x-kubernetes-preserve-unknown-fields: true
        required:
        - spec
        type: object
    served: true
    storage: true
    subresources:
      status: {}
---

        `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).
    *   Added step granularity to `BackupAndRestore` callback for handling
        distributed training failures & restarts. The training state can now be

the sparse tensors have been concatenated along new dimensions, one for each
batch.

The output `SparseTensor` object's shape values for the original dimensions
are the max across the input `SparseTensor` objects' shape values for the
corresponding dimensions. The new dimensions match the size of the batch.

The input `SparseTensor` objects' indices are assumed ordered in

		{"NewPackage", Func, 0},
		{"NewScope", Func, 0},
		{"Node", Type, 0},
		{"NotNilFilter", Func, 0},
		{"ObjKind", Type, 0},
		{"Object", Type, 0},
		{"Object.Data", Field, 0},
		{"Object.Decl", Field, 0},
		{"Object.Kind", Field, 0},
		{"Object.Name", Field, 0},
		{"Object.Type", Field, 0},
		{"Package", Type, 0},
		{"Package.Files", Field, 0},
		{"Package.Imports", Field, 0},
		{"Package.Name", Field, 0},

		if tc.err == "" {
			if len(errs) != 0 {
				t.Errorf("unexpected invalid for test: %s\nErrors returned: %+v\nLocal diff of test objects (-old +new):\n%s", tc.name, errs, cmp.Diff(tc.old, tc.new))
			}
		} else {
			if len(errs) == 0 {
				t.Errorf("unexpected valid for test: %s\nLocal diff of test objects (-old +new):\n%s", tc.name, cmp.Diff(tc.old, tc.new))

FFF9..FFFB    ; disallowed                             # 3.0  INTERLINEAR ANNOTATION ANCHOR..INTERLINEAR ANNOTATION TERMINATOR
FFFC          ; disallowed                             # 2.1  OBJECT REPLACEMENT CHARACTER
FFFD          ; disallowed                             # 1.1  REPLACEMENT CHARACTER
FFFE..FFFF    ; disallowed                             # 1.1  <noncharacter-FFFE>..<noncharacter-FFFF>