parameters: (self, idx_in_batch, epoch_idx, batch_size = 0, lead = 0)
return_statement: return (arg,)
docstring: Generate information to be passed to ES callback.
  
          Args:
              idx_in_batch: Index in batch for per-sample mode, None indicates batch mode where we
              pass only the iteration number.
              lead: how many batches ahead is this job wrt actual iteration
docstring_summary: Generate information to be passed to ES callback.
docstring_tokens: Generate information to be passed to ES callback .
function: def callback_args(self, idx_in_batch, epoch_idx, batch_size = 0, lead = 0):
          """Generate information to be passed to ES callback.
  
          Args:
              idx_in_batch: Index in batch for per-sample mode, None indicates batch mode where we
              pass only the iteration number.
              lead: how many batches ahead is this job wrt actual iteration
          """
          if not self.accepts_arg:
              return ()
          if idx_in_batch is not None:
              arg = nvidia.dali.types.SampleInfo(
                  self.current_sample + idx_in_batch + batch_size * lead,
                  idx_in_batch,
                  self.current_iter + lead,
                  epoch_idx)
          elif self.batch_info:
              arg = nvidia.dali.types.BatchInfo(
                  self.current_iter + lead,
                  epoch_idx)
          else:
              arg = self.current_iter + lead
          return (arg,)
function_tokens: def callback_args ( self , idx_in_batch , epoch_idx , batch_size = 0 , lead = 0 ) : if not self . accepts_arg : return ( ) if idx_in_batch is not None : arg = nvidia . dali . types . SampleInfo ( self . current_sample + idx_in_batch + batch_size * lead , idx_in_batch , self . current_iter + lead , epoch_idx ) elif self . batch_info : arg = nvidia . dali . types . BatchInfo ( self . current_iter + lead , epoch_idx ) else : arg = self . current_iter + lead return ( arg , )