class_docstr: A callback that stores plots of the training and multiple validation losses 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).
      dataset_names_val: The names of the datasets that the validation loss should be plotted for.
class_docstr_tok: ["A","callback","that","stores","plots","of","the","training","and","multiple","validation","losses","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",")",".","dataset_names_val",":","The","names","of","the","datasets","that","the","validation","loss","should","be","plotted","for","."]
code: def __init__(self, dataset_names_val, **kwargs):
      super().__init__(**kwargs)
      self.dataset_names_val = dataset_names_val
  
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 1 to create a new CheatLossPlotLoggerCallback object from inherited base classes: LossPlotLoggerCallback with example: obj = CheatLossPlotLoggerCallback(dataset_names_val)