class_docstr: A callback which logs training progress in terms of steps vs epochs. This is helpful to visualize when the end of data occurs across epochs, especially for iterable datasets.
  This callback writes to the logger at the beginning of training, and at the end of every epoch.
  
  Args:
      loggers: Either a :class:`torchtnt.loggers.logger.MetricLogger` or
          list of :class:`torchtnt.loggers.logger.MetricLogger`
code: def __init__(
      self,
      loggers: Union[MetricLogger, List[MetricLogger]],
  ) -> None:
      if not isinstance(loggers, list):
          loggers = [loggers]
      self._loggers: List[MetricLogger] = loggers
  
file_path: tnt/torchtnt/framework/callbacks/train_progress_monitor.py
imports: from typing import List, Union
  from torchtnt.framework.callback import Callback
  from torchtnt.framework.state import State
  from torchtnt.framework.unit import TTrainUnit
  from torchtnt.utils.loggers.logger import MetricLogger
  from torchtnt.utils.progress import Progress
label_desc: Use image node_id 1 to create a new TrainProgressMonitor object from inherited base classes: Callback with example: obj = TrainProgressMonitor(loggers)