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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 create_embedded_meth(self):
 """ Build the estimator methods or functions. Returns ------- :return : string The built methods as merged string. """

        # Generate related trees:
        fns = []
        for idx, estimator in enumerate(self.estimators):
            tree = self.create_single_method(idx, estimator)
            fns.append(tree)
        fns = '\n'.join(fns)

        # Generate method or function names:
        fn_names = ''
        if self.target_language in ['c', 'java']:
            fn_names = []
            temp_method_calls = self.temp('embedded.method_calls', n_indents=2,
                                          skipping=True)
            for idx, estimator in enumerate(self.estimators):
                cl_name = self.class_name
                fn_name = self.method_name + '_' + str(idx)
                fn_name = temp_method_calls.format(class_name=cl_name,
                                                   method_name=fn_name,
                                                   method_index=idx)
                fn_names.append(fn_name)
            fn_names = '\n'.join(fn_names)
            fn_names = self.indent(fn_names, n_indents=1, skipping=True)

        # Merge generated content:
        n_indents = 1 if self.target_language in ['java', 'js'] else 0
        temp_method = self.temp('embedded.method')
        method = temp_method.format(method_name=self.method_name,
                                    class_name=self.class_name,
                                    method_calls=fn_names, methods=fns,
                                    n_estimators=self.n_estimators,
                                    n_classes=self.n_classes)
        return self.indent(method, n_indents=n_indents, skipping=True)