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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 check_statement(self, stmt, max_paths=1, max_path_length=5):
 """Check a single Statement against the model. Parameters stmt : indra.statements.Statement The Statement to check. max_paths : Optional[int] The maximum number of specific paths to return for each Statement to be explained. Default: 1 max_path_length : Optional[int] The maximum length of specific paths to return. Default: 5 Returns ------- boolean True if the model satisfies the Statement. """

        # Make sure the influence map is initialized
        self.get_im()
        # Check if this is one of the statement types that we can check
        if not isinstance(stmt, (Modification, RegulateAmount,
                                 RegulateActivity, Influence)):
            return PathResult(False, 'STATEMENT_TYPE_NOT_HANDLED',
                              max_paths, max_path_length)
        # Get the polarity for the statement
        if isinstance(stmt, Modification):
            target_polarity = -1 if isinstance(stmt, RemoveModification) else 1
        elif isinstance(stmt, RegulateActivity):
            target_polarity = 1 if stmt.is_activation else -1
        elif isinstance(stmt, RegulateAmount):
            target_polarity = -1 if isinstance(stmt, DecreaseAmount) else 1
        elif isinstance(stmt, Influence):
            target_polarity = -1 if stmt.overall_polarity() == -1 else 1
        # Get the subject and object (works also for Modifications)
        subj, obj = stmt.agent_list()
        # Get a list of monomer patterns matching the subject FIXME Currently
        # this will match rules with the corresponding monomer pattern on it.
        # In future, this statement should (possibly) also match rules in which
        # 1) the agent is in its active form, or 2) the agent is tagged as the
        # enzyme in a rule of the appropriate activity (e.g., a phosphorylation
        # rule) FIXME
        if subj is not None:
            subj_mps = list(pa.grounded_monomer_patterns(self.model, subj,
                                                    ignore_activities=True))
            if not subj_mps:
                logger.debug('No monomers found corresponding to agent %s' %
                             subj)
                return PathResult(False, 'SUBJECT_MONOMERS_NOT_FOUND',
                                  max_paths, max_path_length)
        else:
            subj_mps = [None]
        # Observables may not be found for an activation since there may be no
        # rule in the model activating the object, and the object may not have
        # an "active" site of the appropriate type
        obs_names = self.stmt_to_obs[stmt]
        if not obs_names:
            logger.debug("No observables for stmt %s, returning False" % stmt)
            return PathResult(False, 'OBSERVABLES_NOT_FOUND',
                              max_paths, max_path_length)
        for subj_mp, obs_name in itertools.product(subj_mps, obs_names):
            # NOTE: Returns on the path found for the first enz_mp/obs combo
            result = self._find_im_paths(subj_mp, obs_name, target_polarity,
                                         max_paths, max_path_length)
            # If a path was found, then we return it; otherwise, that means
            # there was no path for this observable, so we have to try the next
            # one
            if result.path_found:
                return result
        # If we got here, then there was no path for any observable
        return PathResult(False, 'NO_PATHS_FOUND',
                          max_paths, max_path_length)