def merge_in_place(self, others):
new_model_names = []<EOL>for predictor in others:<EOL><INDENT>for model in predictor.class1_pan_allele_models:<EOL><INDENT>model_name = self.model_name(<EOL>"<STR_LIT>",<EOL>len(self.class1_pan_allele_models))<EOL>self.class1_pan_allele_models.append(model)<EOL>row = pandas.Series(collections.OrderedDict([<EOL>("<STR_LIT>", model_name),<EOL>("<STR_LIT>", "<STR_LIT>"),<EOL>("<STR_LIT>", json.dumps(model.get_config())),<EOL>("<STR_LIT>", model),<EOL>])).to_frame().T<EOL>self._manifest_df = pandas.concat(<EOL>[self.manifest_df, row], ignore_index=True)<EOL>new_model_names.append(model_name)<EOL><DEDENT>for allele in predictor.allele_to_allele_specific_models:<EOL><INDENT>if allele not in self.allele_to_allele_specific_models:<EOL><INDENT>self.allele_to_allele_specific_models[allele] = []<EOL><DEDENT>current_models = self.allele_to_allele_specific_models[allele]<EOL>for model in predictor.allele_to_allele_specific_models[allele]:<EOL><INDENT>model_name = self.model_name(allele, len(current_models))<EOL>row = pandas.Series(collections.OrderedDict([<EOL>("<STR_LIT>", model_name),<EOL>("<STR_LIT>", allele),<EOL>("<STR_LIT>", json.dumps(model.get_config())),<EOL>("<STR_LIT>", model),<EOL>])).to_frame().T<EOL>self._manifest_df = pandas.concat(<EOL>[self.manifest_df, row], ignore_index=True)<EOL>current_models.append(model)<EOL>new_model_names.append(model_name)<EOL><DEDENT><DEDENT><DEDENT>self.clear_cache()<EOL>return new_model_names<EOL>
Add the models present other predictors into the current predictor.

Parameters
----------
others : list of Class1AffinityPredictor
    Other predictors to merge into the current predictor.

Returns
-------
list of string : names of newly added models