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

  @Override
  Iterable<ImmutableTable<Character, Integer, String>> getTestInstances() {
    return ImmutableList.<ImmutableTable<Character, Integer, String>>of(SPARSE, DENSE);
  }

  public void testCellSet() {

		}
	}

	n = len(b0) - len(b)
	switch {
	case err == io.EOF:
		return n, errMissData // Less data in dense file than sparse file
	case err != nil:
		return n, err
	case sr.logicalRemaining() == 0 && sr.physicalRemaining() > 0:
		return n, errUnrefData // More data in dense file than sparse file
	case finished:
		return n, io.EOF
	default:
		return n, nil
	}
}

  static <R, C, V> RegularImmutableTable<R, C, V> forOrderedComponents(
      ImmutableList<Cell<R, C, V>> cellList,
      ImmutableSet<R> rowSpace,
      ImmutableSet<C> columnSpace) {
    // use a dense table if more than half of the cells have values
    // TODO(gak): tune this condition based on empirical evidence
    return (cellList.size() > (((long) rowSpace.size() * columnSpace.size()) / 2))

import com.google.errorprone.annotations.Immutable;
import com.google.j2objc.annotations.WeakOuter;
import java.util.Map;
import org.jspecify.annotations.Nullable;

/** A {@code RegularImmutableTable} optimized for dense data. */
@GwtCompatible
@Immutable(containerOf = {"R", "C", "V"})
final class DenseImmutableTable<R, C, V> extends RegularImmutableTable<R, C, V> {
  private final ImmutableMap<R, Integer> rowKeyToIndex;

We are going to start from where we left in the previous chapter and increment it.

## About JWT { #about-jwt }

JWT means "JSON Web Tokens".

It's a standard to codify a JSON object in a long dense string without spaces. It looks like this:

```
eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c
```

 * Second, it is always backed by an array large enough to hold a value for every possible
 * combination of row and column keys. (This is rarely optimal unless the table is extremely dense.)
 * Finally, every possible combination of row and column keys is always considered to have a value
 * associated with it: It is not possible to "remove" a value, only to replace it with {@code null},

 * Second, it is always backed by an array large enough to hold a value for every possible
 * combination of row and column keys. (This is rarely optimal unless the table is extremely dense.)
 * Finally, every possible combination of row and column keys is always considered to have a value
 * associated with it: It is not possible to "remove" a value, only to replace it with {@code null},

    *   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.

*   `tf.nn.embedding_lookup_sparse`

    *   Optimized this function for some cases by fusing internal operations.

*   `tf.saved_model.SaveOptions`

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