field.NewValuePool.Put(values[idx])
	}
}

// ScanMode scan data mode
type ScanMode uint8

// scan modes
const (
	ScanInitialized         ScanMode = 1 << 0 // 1
	ScanUpdate              ScanMode = 1 << 1 // 2
	ScanOnConflictDoNothing ScanMode = 1 << 2 // 4
)

// Scan scan rows into db statement
func Scan(rows Rows, db *DB, mode ScanMode) {
	var (
		columns, _          = rows.Columns()

	return s.err
}

// Bytes returns the most recent token generated by a call to [Scanner.Scan].
// The underlying array may point to data that will be overwritten
// by a subsequent call to Scan. It does no allocation.
func (s *Scanner) Bytes() []byte {
	return s.token
}

// Text returns the most recent token generated by a call to [Scanner.Scan]
// as a newly allocated string holding its bytes.
func (s *Scanner) Text() string {

  TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
  TF_ImportGraphDefOptionsAddReturnOutput(opts, "feed", 0);
  TF_ImportGraphDefOptionsAddReturnOutput(opts, "scalar", 0);
  EXPECT_EQ(2, TF_ImportGraphDefOptionsNumReturnOutputs(opts));
  TF_ImportGraphDefOptionsAddReturnOperation(opts, "scalar");
  EXPECT_EQ(1, TF_ImportGraphDefOptionsNumReturnOperations(opts));

 * and processes them to create thumbnail images based on configured dimensions
 * and format settings.
 *
 * <p>The generator validates image MIME types, processes image data through
 * ImageIO operations, and applies scaling and cropping to generate thumbnails
 * that meet the specified size requirements.</p>
 *
 */
public class HtmlTagBasedGenerator extends BaseThumbnailGenerator {

  TFE_OpSetAttrType(fill_op, "T", TF_FLOAT);
  TFE_OpSetAttrType(fill_op, "Tshape", TF_INT32);

  float five = 5.0;
  TFE_TensorHandle* scalar = TestScalarTensorHandle(tfe_context_, five);
  TF_Tensor* scalarTensor = TFE_TensorHandleResolve(scalar, status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  CheckOutputShapes(fill_op,

            k <= bias -> TMIN
            k >= bias + TMAX -> TMAX
            else -> k - bias
          }
        if (digit < t) break
        val scaleW = BASE - t
        if (w > Int.MAX_VALUE / scaleW) return false // Prevent overflow.
        w *= scaleW
      }
      bias = adapt(i - oldi, codePoints.size + 1, oldi == 0)
      val deltaN = i / (codePoints.size + 1)

   *
   * <p>Zero is treated as having log2 == 0.
   */
  static double randomDouble(int maxExponent) {
    double result = RANDOM_SOURCE.nextDouble();
    result = Math.scalb(result, RANDOM_SOURCE.nextInt(maxExponent + 1));
    return RANDOM_SOURCE.nextBoolean() ? result : -result;
  }

  /** Returns a random integer between zero and {@code MAX_EXPONENT}. */
  static int randomExponent() {

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  Run({{func_feed, Int32Tensor(3)}}, func_op, 2 + 3);
  VerifyFDef(
      {"add_0", "scalar"}, M({{"feed1"}, {"feed2"}}), M({{"add"}}),
      {{"feed1", "add_0:0"}, {"feed2", "add_0:1"}, {"add_0:sum:0", "add"}},
      {{"^scalar", "add_0:2"}});
}

TEST_F(CApiFunctionTest, ControlDependencyOutsideOfBody) {
  /*
   *                  |  |    scalar
   *                  |  |    .
   *                  v  v   . <---- control dependency

    // user-specified -XX:+DisableExplicitGC.
    //
    // TODO(user): Consider using
    // java/lang/management/OperatingSystemMXBean.html#getSystemLoadAverage()
    //
    // TODO(user): Consider scaling by number of mutator threads,
    // e.g. using Thread#activeCount()
    return max(10L, Runtime.getRuntime().totalMemory() / (32L * 1024L * 1024L));
  }

  /**