<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def preassemble(self, filters=None, grounding_map=None):
 """Preassemble the Statements collected in the model. Use INDRA's GroundingMapper, Preassembler and BeliefEngine on the IncrementalModel and save the unique statements and the top level statements in class attributes. Currently the following filter options are implemented: - grounding: require that all Agents in statements are grounded - human_only: require that all proteins are human proteins - prior_one: require that at least one Agent is in the prior model - prior_all: require that all Agents are in the prior model Parameters filters : Optional[list[str]] A list of filter options to apply when choosing the statements. See description above for more details. Default: None grounding_map : Optional[dict] A user supplied grounding map which maps a string to a dictionary of database IDs (in the format used by Agents' db_refs). """

        stmts = self.get_statements()

        # Filter out hypotheses
        stmts = ac.filter_no_hypothesis(stmts)

        # Fix grounding
        if grounding_map is not None:
            stmts = ac.map_grounding(stmts, grounding_map=grounding_map)
        else:
            stmts = ac.map_grounding(stmts)

        if filters and ('grounding' in filters):
            stmts = ac.filter_grounded_only(stmts)

        # Fix sites
        stmts = ac.map_sequence(stmts)

        if filters and 'human_only' in filters:
            stmts = ac.filter_human_only(stmts)

        # Run preassembly
        stmts = ac.run_preassembly(stmts, return_toplevel=False)

        # Run relevance filter
        stmts = self._relevance_filter(stmts, filters)

        # Save Statements
        self.assembled_stmts = stmts