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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 process_phosphorylation_statements(self):
 """Looks for Phosphorylation events in the graph and extracts them into INDRA statements. In particular, looks for a Positive_regulation event node with a child Phosphorylation event node. If Positive_regulation has an outgoing Cause edge, that's the subject If Phosphorylation has an outgoing Theme edge, that's the object If Phosphorylation has an outgoing Site edge, that's the site """

        G = self.G
        statements = []

        pwcs = self.find_event_parent_with_event_child('Positive_regulation',
                                                       'Phosphorylation')
        for pair in pwcs:
            (pos_reg, phos) = pair
            cause = self.get_entity_text_for_relation(pos_reg, 'Cause')
            theme = self.get_entity_text_for_relation(phos, 'Theme')
            print('Cause:', cause, 'Theme:', theme)

            # If the trigger word is dephosphorylate or similar, then we
            # extract a dephosphorylation statement
            trigger_word = self.get_entity_text_for_relation(phos,
                                                             'Phosphorylation')
            if 'dephos' in trigger_word:
                deph = True
            else:
                deph = False

            site = self.get_entity_text_for_relation(phos, 'Site')

            theme_node = self.get_related_node(phos, 'Theme')
            assert(theme_node is not None)
            evidence = self.node_to_evidence(theme_node, is_direct=False)

            if theme is not None:
                if deph:
                    statements.append(Dephosphorylation(s2a(cause),
                                      s2a(theme), site, evidence=evidence))
                else:
                    statements.append(Phosphorylation(s2a(cause),
                                      s2a(theme), site, evidence=evidence))
        return statements