class_docstr: Base Recurrent
  
  Base class for all recurrent-based models. The forecasts are produced sequentially between
  windows.
  
  This class implements the basic functionality for all windows-based models, including:
  - PyTorch Lightning's methods training_step, validation_step, predict_step. <br>
  - fit and predict methods used by NeuralForecast.core class. <br>
  - sampling and wrangling methods to sequential windows. <br>
class_docstr_tok: ["Base","Recurrent","Base","class","for","all","recurrent-based","models",".","The","forecasts","are","produced","sequentially","between","windows",".","This","class","implements","the","basic","functionality","for","all","windows-based","models",",","including",":","-","PyTorch","Lightning","'s","methods","training_step",",","validation_step",",","predict_step",".","<","br",">","-","fit","and","predict","methods","used","by","NeuralForecast.core","class",".","<","br",">","-","sampling","and","wrangling","methods","to","sequential","windows",".","<","br",">"]
code: def set_test_size(self, test_size):
      self.test_size = test_size
  
imports: import random
  import warnings
  import numpy
  import torch
  import torch.nn
  import pytorch_lightning
  from pytorch_lightning.callbacks import TQDMProgressBar
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from ._scalers import TemporalNorm
  from ..tsdataset import TimeSeriesDataModule
  from ..utils import get_indexer_raise_missing
label_desc: Use image node_id 16 for calling the BaseRecurrent obj's underlying member method code with example usage: obj.set_test_size(test_size) without return types