// Keywords for GNU sparse files in a PAX extended header.
	paxGNUSparse          = "GNU.sparse."
	paxGNUSparseNumBlocks = "GNU.sparse.numblocks"
	paxGNUSparseOffset    = "GNU.sparse.offset"
	paxGNUSparseNumBytes  = "GNU.sparse.numbytes"
	paxGNUSparseMap       = "GNU.sparse.map"
	paxGNUSparseName      = "GNU.sparse.name"
	paxGNUSparseMajor     = "GNU.sparse.major"
	paxGNUSparseMinor     = "GNU.sparse.minor"

//
// The table's lower portion shows specialized features of each format,
// such as supported string encodings, support for sub-second timestamps,
// or support for sparse files.
//
// The Writer currently provides no support for sparse files.
type Format int

// Constants to identify various tar formats.
const (
	// Deliberately hide the meaning of constants from public API.

			}, nil},
			testClose{nil},
		},
		// TODO(dsnet): Re-enable this test when adding sparse support.
		// See https://golang.org/issue/22735
		/*
			}, {
				file: "testdata/gnu-nil-sparse-data.tar",
				tests: []testFnc{
					testHeader{Header{
						Typeflag:    TypeGNUSparse,
						Name:        "sparse.db",
						Size:        1000,
						SparseHoles: []sparseEntry{{Offset: 1000, Length: 0}},

# Where component owners are known, add them here.

/tensorflow/c/eager @qqfish
/tensorflow/core/common_runtime/eager @qqfish
/tenosrflow/core/debug @caisq
/tensorflow/core/kernels/mkl/ @penpornk
/tensorflow/core/kernels/sparse/ @penpornk
/tensorflow/core/nccl/ @azaks2 @chsigg
/tensorflow/python/autograph/ @mdanatg
/tensorflow/python/debug @caisq
/tensorflow/python/eager @rohan100jain
/tensorflow/tools/docs/ @markdaoust

Yfoo test2/foo fewafewa fewa feawfehahaha hahaafwe hahafawe hahawafe a fwefewa test2/sparse...

  private static final ImmutableSet<Integer> COLUMN_SPACE = ImmutableSet.of(1, 2);

  private static final SparseImmutableTable<Character, Integer, String> SPARSE =
      new SparseImmutableTable<>(CELLS.asList(), ROW_SPACE, COLUMN_SPACE);

  private static final DenseImmutableTable<Character, Integer, String> DENSE =

      .isEqualTo("<< 0x00000003 10000 DATA          COMPRESSED")
  }

  /**
   * Ensures that valid flag combinations appear visually correct, and invalid show in hex.  This
   * also demonstrates how sparse the lookup table is.
   */
  @Test
  fun allFormattedFlagsWithValidBits() {
    val formattedFlags = mutableListOf<String>() // Highest valid flag is 0x20.

### Bug Fixes and Other Changes

*   Add TensorFlow Quantizer to TensorFlow pip package.

*   `tf.sparse.segment_sum` `tf.sparse.segment_mean` `tf.sparse.segment_sqrt_n` `SparseSegmentSum/Mean/SqrtN[WithNumSegments]`

    *   Added `sparse_gradient` option (default=false) that makes the gradient of these functions/ops sparse (`IndexedSlices`) instead of dense (`Tensor`), using new `SparseSegmentSum/Mean/SqrtNGradV2` ops.

import com.google.errorprone.annotations.Immutable;
import java.util.LinkedHashMap;
import java.util.Map;
import java.util.Map.Entry;

/** A {@code RegularImmutableTable} optimized for sparse data. */
@GwtCompatible
@Immutable(containerOf = {"R", "C", "V"})
final class SparseImmutableTable<R, C, V> extends RegularImmutableTable<R, C, V> {
  static final ImmutableTable<Object, Object, Object> EMPTY =

  /**
   * Closed addressing tends to perform well even with high load factors. Being conservative here
   * ensures that the table is still likely to be relatively sparse (hence it misses fast) while
   * saving space.
   */
  @VisibleForTesting static final double MAX_LOAD_FACTOR = 1.2;

  /**
   * Maximum allowed false positive probability of detecting a hash flooding attack given random