How to use the mhcflurry.Class1AffinityPredictor function in mhcflurry

def test_mhcflurry():
    predictor = MHCflurry(alleles=[DEFAULT_ALLELE])
    binding_predictions = predictor.predict_subsequences(
        protein_sequence_dict,
        peptide_lengths=[9])
    eq_(4, len(binding_predictions),
        "Expected 4 binding predictions from %s" % (binding_predictions,))

    prediction_scores = {
        (x.peptide, x.allele): x.affinity for x in binding_predictions
    }

    predictor = Class1AffinityPredictor.load()
    # test one prediction at a time to make sure there's no peptide/allele mixup
    for (peptide, allele), affinity in prediction_scores.items():
        prediction = predictor.predict([peptide], allele=allele)
        assert len(prediction) == 1
        # testing.assert_almost_equal(round(prediction[0], 2), round(affinity, 2))
        testing.assert_almost_equal(prediction[0], affinity)

"""
        # moving import here since the mhcflurry package imports
        # Keras and its backend (either Theano or TF) which end up
        # slowing down responsive for any CLI application using MHCtools
        from mhcflurry import Class1AffinityPredictor
        BasePredictor.__init__(
            self,
            alleles=alleles,
            default_peptide_lengths=default_peptide_lengths,
            min_peptide_length=8,
            max_peptide_length=15)
        if predictor:
            self.predictor = predictor
        elif models_path:
            logging.info("Loading MHCflurry models from %s" % models_path)
            self.predictor = Class1AffinityPredictor.load(models_path)
        else:
            self.predictor = Class1AffinityPredictor.load()

        # relying on BasePredictor and MHCflurry to both normalize
        # allele names the same way using mhcnames
        for allele in self.alleles:
            if allele not in self.predictor.supported_alleles:
                raise UnsupportedAllele(allele)