return accuracy, loss_value

  iterator = iter(ds_train)
  accuracy = 0.0
  for step in range(flags.FLAGS.train_steps):
    accuracy, loss_value = distributed_train_step(next(iterator))
    if step % display_step == 0:
      tf.print('Step %d:' % step)
      tf.print('    Loss = %f' % loss_value)
      tf.print('    Batch accuracy = %f' % accuracy)

	i -= -1
	if i < 0 || i >= Accuracy(len(_Accuracy_index)-1) {
		return "Accuracy(" + strconv.FormatInt(int64(i+-1), 10) + ")"
	}
	return _Accuracy_name[_Accuracy_index[i]:_Accuracy_index[i+1]]

class MnistTrainTest(test_util.TensorFlowTestCase, parameterized.TestCase):

  @combinations.generate(combinations.combine(strategy=strategies))
  def testMnistTrain(self, strategy):
    accuracy = mnist_train.main(strategy)
    self.assertGreater(accuracy, 0.7, 'accuracy sanity check')


if __name__ == '__main__':

// Complex circular sine
//
// DESCRIPTION:
//
// If
//     z = x + iy,
//
// then
//
//     w = sin x  cosh y  +  i cos x sinh y.
//
// csin(z) = -i csinh(iz).
//
// ACCURACY:
//
//                      Relative error:
// arithmetic   domain     # trials      peak         rms
//    DEC       -10,+10      8400       5.3e-17     1.3e-17
//    IEEE      -10,+10     30000       3.8e-16     1.0e-16

//       x + x**3 P(x**2)/Q(x**2)
// is employed in the basic interval [0, pi/4].
//
// ACCURACY:
//                      Relative error:
// arithmetic   domain     # trials      peak         rms
//    DEC      +-1.07e9      44000      4.1e-17     1.0e-17
//    IEEE     +-1.07e9      30000      2.9e-16     8.1e-17
//
// Partial loss of accuracy begins to occur at x = 2**30 = 1.074e9.  The loss

const floatGobVersion byte = 1

// GobEncode implements the [encoding/gob.GobEncoder] interface.
// The [Float] value and all its attributes (precision,
// rounding mode, accuracy) are marshaled.
func (x *Float) GobEncode() ([]byte, error) {
	if x == nil {
		return nil, nil
	}

	// determine max. space (bytes) required for encoding

//go:generate stringer -type=RoundingMode

// Accuracy describes the rounding error produced by the most recent
// operation that generated a [Float] value, relative to the exact value.
type Accuracy int8

// Constants describing the [Accuracy] of a [Float].
const (
	Below Accuracy = -1
	Exact Accuracy = 0
	Above Accuracy = +1
)

//go:generate stringer -type=Accuracy

//      Given x, find r and integer k such that
//
//               x = k*ln2 + r,  |r| <= 0.5*ln2.
//
//      Here r will be represented as r = hi-lo for better
//      accuracy.
//
//   2. Approximation of exp(r) by a special rational function on
//      the interval [0,0.34658]:
//      Write
//          R(r**2) = r*(exp(r)+1)/(exp(r)-1) = 2 + r*r/6 - r**4/360 + ...

//	In order to guarantee error in log below 1ulp, we compute log by
//		log(1+f) = f - s*(f - R)		(if f is not too large)
//		log(1+f) = f - (hfsq - s*(hfsq+R)).	(better accuracy)
//
//	3. Finally,  log(x) = k*Ln2 + log(1+f).
//			    = k*Ln2_hi+(f-(hfsq-(s*(hfsq+R)+k*Ln2_lo)))
//	   Here Ln2 is split into two floating point number:
//			Ln2_hi + Ln2_lo,

// Complex circular arc sine
//
// DESCRIPTION:
//
// Inverse complex sine:
//                               2
// w = -i clog( iz + csqrt( 1 - z ) ).
//
// casin(z) = -i casinh(iz)
//
// ACCURACY:
//
//                      Relative error:
// arithmetic   domain     # trials      peak         rms
//    DEC       -10,+10     10100       2.1e-15     3.4e-16
//    IEEE      -10,+10     30000       2.2e-14     2.7e-15