class_docstr: Handles training restarts.
  
  Particularly useful when sharing parameters (and checkpoints) between models.
  
  Args:
    model_mode (str): Model "mode". Different modes have different local step
        counters, but the same global step counter. Also used in log messages.
    checkpoint_dir (str): Model checkpoint directory. Global step is inferred
        from the name of the last checkpoint.
    target_local_step (int): Local step to train the model up to.
  
  Attributes:
    model_mode (str): See args.
    checkpoint_dir (str): See args.
    target_local_step (int): See args.
    target_global_step (int): Calculated global step to train the model up to.
    should_skip (bool): Whether training should be skipped because the number of
        local steps already done is higher than the target. This happens during
        restarts.
    steps_to_go: how many steps to go.
    restarting (bool): Whether the current epoch of training has been
        interrupted and is being restarted.
code: def _read_counters(self):
      try:
          with tf.gfile.Open(self._counter_path, "r") as f:
              return tuple(
                  int(counter) for counter in f.read().split(" ")
              )
      except tf.errors.NotFoundError:
          return (0, -1)
  
file_path: tensor2tensor/tensor2tensor/rl/restarter.py
imports: import contextlib
  import os
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling the Restarter obj's underlying member method code with example usage: obj._read_counters() and returns: tuple, int,  unknown