code: def epochs_trained(self):
      current_epoch = self.current_epoch
  
      # For PTL < 1.6.0 we have to adjust:
      if not pl_160_or_above and (
          self.current_epoch or self.global_step
      ):
          current_epoch += 1
  
      return current_epoch
  
imports: from abc import ABC, abstractmethod
  from functools import wraps
  from typing import Any, Dict, Optional, Sequence, Tuple, Union
  import pytorch_lightning
  import torch
  import torch.nn
  import torchmetrics
  from joblib import Parallel, delayed
  from darts.logging import get_logger, raise_if, raise_log
  from darts.models.components.layer_norm_variants import RINorm
  from darts.timeseries import TimeSeries
  from darts.utils.likelihood_models import Likelihood
  from darts.utils.timeseries_generation import _build_forecast_series
  from darts.utils.torch import MonteCarloDropout
label_desc: Use image node_id 21 for calling the PLForecastingModule obj's underlying member method code with example usage: obj.epochs_trained() and returns: current_epoch