class_docstr: Handles the main model training procedure. This includes performing evaluation on the dev set at regular
  intervals during training as well as evaluation on the test set at the end of training.
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docstr: Verify in distributed training if all ranks still have data left. We send a "1" from here if this rank has data
  and a "0" if a process has none .
  If all ranks have data, they'll all send a 1, our sum equals world_size and we continue training.
  If not, we must break the loop for those who still have data to synchronize again.
  :param has_data: bool, whether the current rank has still data
  :param step: int, current step (only used for logging)
  :return: bool, whether all ranks have training data left
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code: def _all_ranks_have_data(self, has_data, step=None):
      """
      Verify in distributed training if all ranks still have data left. We send a "1" from here if this rank has data
      and a "0" if a process has none .
      If all ranks have data, they'll all send a 1, our sum equals world_size and we continue training.
      If not, we must break the loop for those who still have data to synchronize again.
      :param has_data: bool, whether the current rank has still data
      :param step: int, current step (only used for logging)
      :return: bool, whether all ranks have training data left
      """
  
      if has_data:
          ranks_with_data = torch.ones(1).to(self.device)
      else:
          ranks_with_data = torch.zeros(1).to(self.device)
  
      torch.distributed.all_reduce(
          ranks_with_data, op=torch.distributed.ReduceOp.SUM
      )
  
      if ranks_with_data < torch.distributed.get_world_size():
          if step is not None:
              logger.info(
                  f"Stopping epoch {self.from_epoch} at step {step} for rank {self.local_rank} since at least one other rank "
                  f"(~ one GPU) in distributed training doesn't have any more batches... "
              )
          return False
      else:
          return True
  
imports: import logging
  import sys
  import torch
  from pathlib import Path
  from tqdm import tqdm
  import numpy
  import shutil
  import dill
  from farm.utils import MLFlowLogger
  from farm.utils import GracefulKiller, set_all_seeds
  from farm.eval import Evaluator
  from farm.data_handler.data_silo import DataSilo
  from farm.visual.ascii.images import GROWING_TREE
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.biadaptive_model import BiAdaptiveModel
  from farm.modeling.optimization import get_scheduler
label_desc: Use image node_id 11 for calling the Trainer obj's underlying member method code with example usage: obj._all_ranks_have_data(has_data, step) and returns: False, True