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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def filter_networks(self, predicate):
 """ 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 """

        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,
        )