code: def filter_networks(self, predicate):
          
          allele_to_allele_specific_models = {}
          for (allele, models) in self.allele_to_allele_specific_models.items():
              allele_to_allele_specific_models[allele] = [
                  m for m in models if predicate(m)
              ]
          class1_pan_allele_models = [
              m for m in self.class1_pan_allele_models if predicate(m)
          ]
  
          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=self.allele_to_fixed_length_sequence,
          )
docs: Return a new Class1AffinityPredictor containing a subset of this
          predictor's neural networks.
  
          Parameters
          ----------
          predicate : Class1NeuralNetwork -> boolean
              Function specifying which neural networks to include
  
          Returns
          -------
          Class1AffinityPredictor