(iterations_per_loop)
return state_ops.assign_add(
      eval_step,
      math_ops.cast(iterations_per_loop - 1, dtype=eval_step.dtype),
      use_locking=True)
Returns an op to increase the eval step for TPU evaluation.

  Args:
    iterations_per_loop: Tensor. The number of eval steps running in TPU system
      before returning to CPU host for each `Session.run`.

  Returns:
    An operation
Returns an op to increase the eval step for TPU evaluation.
Returns an op to increase the eval step for TPU evaluation .
def _increase_eval_step_op(iterations_per_loop):
  """Returns an op to increase the eval step for TPU evaluation.

  Args:
    iterations_per_loop: Tensor. The number of eval steps running in TPU system
      before returning to CPU host for each `Session.run`.

  Returns:
    An operation
  """
  eval_step = evaluation._get_or_create_eval_step()  # pylint: disable=protected-access
  # Estimator evaluate increases 1 by default. So, we increase the difference.
  return state_ops.assign_add(
      eval_step,
      math_ops.cast(iterations_per_loop - 1, dtype=eval_step.dtype),
      use_locking=True)
def _increase_eval_step_op ( iterations_per_loop ) : eval_step = evaluation . _get_or_create_eval_step ( ) # pylint: disable=protected-access # Estimator evaluate increases 1 by default. So, we increase the difference. return state_ops . assign_add ( eval_step , math_ops . cast ( iterations_per_loop - 1 , dtype = eval_step . dtype ) , use_locking = True )