class_docstr: This ensemble acts as a mixer selector. 
  After evaluating accuracy for all internal mixers with the validation data, it sets the best mixer as the underlying model.
code: def special_dispatch(self, mixer, data, args, target, ts_analysis):
      if isinstance(mixer, GluonTSMixer):
          return mixer.model_train_stats.loss_history[-1]
  
file_path: lightwood/lightwood/ensemble/best_of.py
imports: from typing import List, Optional
  import numpy
  import pandas
  from mindsdb_evaluator import evaluate_accuracies
  from lightwood.helpers.log import log
  from type_infer.helpers import is_nan_numeric
  from lightwood.mixer.base import BaseMixer
  from lightwood.ensemble.base import BaseEnsemble
  from lightwood.api.types import PredictionArguments, SubmodelData
  from lightwood.data.encoded_ds import EncodedDs
  from lightwood.mixer import GluonTSMixer
label_desc: Use image node_id 3 for calling the BestOf obj's underlying member method code with example usage: obj.special_dispatch(mixer, data, args, target, ts_analysis) and returns: mixer