class_docstr: A callback that stores plots of the training and macro-averaged validation loss curve every n epochs.
  
  Args:
      every_n_epochs: Specifies how often the plots are generated.
          Setting this to a large value can save time since plotting can be time consuming
          (especially when small datasets are used).
class_docstr_tok: ["A","callback","that","stores","plots","of","the","training","and","macro-averaged","validation","loss","curve","every","n","epochs",".","Args",":","every_n_epochs",":","Specifies","how","often","the","plots","are","generated",".","Setting","this","to","a","large","value","can","save","time","since","plotting","can","be","time","consuming","(","especially","when","small","datasets","are","used",")","."]
code: def on_validation_epoch_end(
      self, trainer: Trainer, pl_module: MetaLightningModule
  ) -> None:
      if (trainer.current_epoch < 1) or (
          trainer.current_epoch % self.every_n_epochs
      ):
          return
      self.plot_loss(trainer=trainer, pl_module=pl_module)
  
imports: from typing import Optional, Union, List
  from pytorch_lightning import Callback, Trainer
  import matplotlib.pyplot
  from meta.data.batch import TripletBatch
  from meta.models.module import MetaLightningModule
  from .common import get_save_dir_from_csvlogger, get_loss_steps
  from meta.data.batch import SeriesBatch
  from meta.vis.forecast import plot_quantile_forecast, plot_forecast_supportset_attention
label_desc: Use image node_id 2 for calling the LossPlotLoggerCallback obj's underlying member method code with example usage: obj.on_validation_epoch_end(trainer, pl_module) without return types