docstr: Refits an already trained model using a new dataset
  Useful when fitting to the full data after testing with cross validation
  :param ts_df The time series data to be used for fitting the model
  :type ts_df pd.DataFrame
  :rtype object
docstr_tok: ["Refits","an","already","trained","model","using","a","new","dataset","Useful","when","fitting","to","the","full","data","after","testing","with","cross","validation",":","param","ts_df","The","time","series","data","to","be","used","for","fitting","the","model",":","type","ts_df","pd.DataFrame",":","rtype","object"]
code: def refit(self, ts_df: pd.DataFrame) -> object:
      """
      Refits an already trained model using a new dataset
      Useful when fitting to the full data after testing with cross validation
      :param ts_df The time series data to be used for fitting the model
      :type ts_df pd.DataFrame
      :rtype object
      """
  
      bestmodel = self.get_best_model(ts_df)
  
      print(
          colorful.BOLD
          + "Refitting data with previously found best parameters"
          + colorful.END
      )
      try:
          self.model = bestmodel.fit(disp=False)
          print(
              "    Best %s metric = %0.1f"
              % (self.scoring, eval("self.model." + self.scoring))
          )
      except Exception as e:
          print(e)
  
      return self
  
imports: from typing import Optional
  import warnings
  from abc import abstractmethod
  import copy
  import pdb
  import numpy
  import pandas
  from pandas.core.generic import NDFrame
  import dask
  import dask.dataframe
  import matplotlib.pyplot
  from sklearn.model_selection import TimeSeriesSplit
  from statsmodels.tsa.statespace.sarimax import SARIMAX
  from ..build_base import BuildBase
  from ...utils import colorful, print_static_rmse, print_dynamic_rmse, print_ts_model_stats
  from ...models.ar_based.param_finder import find_best_pdq_or_PDQ
label_desc: Use image node_id 3 for calling the BuildArimaBase obj's underlying member method code with example usage: obj.refit(ts_df) and returns: self