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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 get_dict(self, only_attributes=None, exclude_attributes=None, df_format=False):
 """Summarize the I-TASSER run in a dictionary containing modeling results and top predictions from COACH Args: only_attributes (str, list):
 Attributes that should be returned. If not provided, all are returned. exclude_attributes (str, list):
 Attributes that should be excluded. df_format (bool):
 If dictionary values should be formatted for a dataframe (everything possible is transformed into strings, int, or float - if something can't be transformed it is excluded) Returns: dict: Dictionary of attributes """


        to_exclude = ['coach_bsites', 'coach_ec', 'coach_go_mf', 'coach_go_bp', 'coach_go_cc']
        if not exclude_attributes:
            excluder = to_exclude
        else:
            excluder = ssbio.utils.force_list(exclude_attributes)
            excluder.extend(to_exclude)

        summary_dict = StructProp.get_dict(self, only_attributes=only_attributes,
                                           exclude_attributes=excluder,
                                           df_format=df_format)

        if self.coach_bsites:
            tmp = {'top_bsite_' + k:v for k, v in self.coach_bsites[0].items()}
            summary_dict.update(tmp)

        if self.coach_ec:
            tmp = {'top_ec_' + k: v for k, v in self.coach_ec[0].items()}
            summary_dict.update(tmp)

        if self.coach_go_mf:
            tmp = {'top_go_mf_' + k: v for k, v in self.coach_go_mf[0].items()}
            summary_dict.update(tmp)

        if self.coach_go_bp:
            tmp = {'top_go_bp_' + k: v for k, v in self.coach_go_bp[0].items()}
            summary_dict.update(tmp)

        if self.coach_go_cc:
            tmp = {'top_go_cc_' + k: v for k, v in self.coach_go_cc[0].items()}
            summary_dict.update(tmp)

        return summary_dict