### 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

 * iterator remains valid.
 *
 * <p>This class requires less memory than the {@link HashBasedTable} and {@link TreeBasedTable}
 * implementations, except when the table is sparse.
 *
 * <p>Null row keys or column keys are not permitted.
 *
 * <p>This class provides methods involving the underlying array structure, where the array indices

 * iterator remains valid.
 *
 * <p>This class requires less memory than the {@link HashBasedTable} and {@link TreeBasedTable}
 * implementations, except when the table is sparse.
 *
 * <p>Null row keys or column keys are not permitted.
 *
 * <p>This class provides methods involving the underlying array structure, where the array indices

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

	// Add new AMZ date formats here.
}

// ErrMalformedDate always returned for dates that cannot be parsed.
var ErrMalformedDate = errors.New("malformed date")

// Parse parses date string via supported amz date formats.
func Parse(amzDateStr string) (time.Time, error) {
	for _, dateFormat := range amzDateFormats {
		amzDate, err := time.Parse(dateFormat, amzDateStr)
		if err == nil {
			return amzDate, nil
		}
	}

}

// FuncExpr represents a function call
type FuncExpr struct {
	SFunc     *SimpleArgFunc `parser:"  @@"`
	Count     *CountFunc     `parser:"| @@"`
	Cast      *CastFunc      `parser:"| @@"`
	Substring *SubstringFunc `parser:"| @@"`
	Extract   *ExtractFunc   `parser:"| @@"`
	Trim      *TrimFunc      `parser:"| @@"`
	DateAdd   *DateAddFunc   `parser:"| @@"`
	DateDiff  *DateDiffFunc  `parser:"| @@"`

	p.errorCount++
	if p.errorCount > 10 && !*flags.AllErrors {
		log.Fatal("too many errors")
	}
}

func (p *Parser) pos() src.XPos {
	return p.ctxt.PosTable.XPos(src.MakePos(p.lex.Base(), uint(p.lineNum), 0))
}

func (p *Parser) Parse() (*obj.Prog, bool) {
	scratch := make([][]lex.Token, 0, 3)
	for {
		word, cond, operands, ok := p.line(scratch)
		if !ok {
			break
		}