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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_term_agents(self):
 """Return dict of INDRA Agents keyed by corresponding TERMs in the EKB. This is meant to be used when entities e.g. "phosphorylated ERK", rather than events need to be extracted from processed natural language. These entities with their respective states are represented as INDRA Agents. Further, each key of the dictionary corresponds to the ID assigned by TRIPS to the given TERM that the Agent was extracted from. Returns ------- agents : dict[str, indra.statements.Agent] Dict of INDRA Agents extracted from EKB. """

        terms = self.tree.findall('TERM')
        agents = {}
        assoc_links = []
        for term in terms:
            term_id = term.attrib.get('id')
            if term_id:
                agent = self._get_agent_by_id(term_id, None)
                agents[term_id] = agent
                # Handle assoc-with links
                aw = term.find('assoc-with')
                if aw is not None:
                    aw_id = aw.attrib.get('id')
                    if aw_id:
                        assoc_links.append((term_id, aw_id))
        # We only keep the target end of assoc with links if both
        # source and target are in the list
        for source, target in assoc_links:
            if target in agents and source in agents:
                agents.pop(source)
        return agents