parameters: (iterations_per_loop)
return_statement: return state_ops.assign_add(
        eval_step,
        math_ops.cast(iterations_per_loop - 1, dtype=eval_step.dtype),
        use_locking=True)
docstring: Returns an op to increase the eval step for TPU evaluation.
  
    Args:
      iterations_per_loop: Tensor. The number of eval steps runnining in TPU
          system before returning to CPU host for each `Session.run`.
  
    Returns:
      An operation
docstring_summary: Returns an op to increase the eval step for TPU evaluation.
docstring_tokens: Returns an op to increase the eval step for TPU evaluation .
function: 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 runnining 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)
function_tokens: 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 )