import java.util.Map.Entry;
import org.jspecify.annotations.Nullable;

/**
 * Simple helper class to build a "sparse" array of objects based on the indexes that were added to
 * it. The array will be from 0 to the maximum index given. All non-set indexes will contain null
 * (so it's not really a sparse array, just a pseudo sparse array). The builder can also return a
 * CharEscaper based on the generated array.
 *
 * @author Sven Mawson

			}, 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}},

  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 =

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 =

      .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.

    return false;
  }

  /**
   * 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 = 1.0;

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

### 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 java.util.Objects;
import java.util.Set;
import org.jspecify.annotations.Nullable;

/**
 * A collection that associates an ordered pair of keys, called a row key and a column key, with a
 * single value. A table may be sparse, with only a small fraction of row key / column key pairs
 * possessing a corresponding value.
 *
 * <p>The mappings corresponding to a given row key may be viewed as a {@link Map} whose keys are

  /**
   * 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
   * input.
   */

import java.util.Objects;
import java.util.Set;
import org.jspecify.annotations.Nullable;

/**
 * A collection that associates an ordered pair of keys, called a row key and a column key, with a
 * single value. A table may be sparse, with only a small fraction of row key / column key pairs
 * possessing a corresponding value.
 *
 * <p>The mappings corresponding to a given row key may be viewed as a {@link Map} whose keys are