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 on_train_epoch_end(self, state: State, unit: TTrainUnit) -> None:
      _write_training_progress(unit.train_progress, self._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 3 for calling the TrainProgressMonitor obj's underlying member method code with example usage: obj.on_train_epoch_end(state, unit) without return types