code: def merge(cls, predictors):
          
          assert len(predictors) > 0
          if len(predictors) == 1:
              return predictors[0]
  
          allele_to_allele_specific_models = collections.defaultdict(list)
          class1_pan_allele_models = []
          allele_to_fixed_length_sequence = predictors[0].allele_to_fixed_length_sequence
  
          for predictor in predictors:
              for (allele, networks) in (
                      predictor.allele_to_allele_specific_models.items()):
                  allele_to_allele_specific_models[allele].extend(networks)
              class1_pan_allele_models.extend(
                  predictor.class1_pan_allele_models)
  
          return Class1AffinityPredictor(
              allele_to_allele_specific_models=allele_to_allele_specific_models,
              class1_pan_allele_models=class1_pan_allele_models,
              allele_to_fixed_length_sequence=allele_to_fixed_length_sequence
          )
docs: Merge the ensembles of two or more `Class1AffinityPredictor` instances.
  
          Note: the resulting merged predictor will NOT have calibrated percentile
          ranks. Call `calibrate_percentile_ranks` on it if these are needed.
  
          Parameters
          ----------
          predictors : sequence of `Class1AffinityPredictor`
  
          Returns
          -------
          `Class1AffinityPredictor` instance