code: def get_embedd_mentions():
      mentions_json = [
          {
              "coref_chain": "HUM16236184328979740",
              "doc_id": "0",
              "mention_context": [
                  "Perennial",
                  "party",
                  "girl",
                  "Tara",
                  "Reid",
                  "checked",
                  "herself",
                  "into",
                  "Promises",
                  "Treatment",
                  "Center",
                  ",",
                  "her",
                  "rep",
                  "told",
                  "People",
                  ".",
              ],
              "mention_head": "Reid",
              "mention_head_lemma": "reid",
              "mention_head_pos": "PROPN",
              "mention_id": "0",
              "mention_index": -1,
              "mention_ner": "PERSON",
              "mention_type": "HUM",
              "predicted_coref_chain": None,
              "score": -1.0,
              "sent_id": 0,
              "tokens_number": [3, 4],
              "tokens_str": "Tara Reid",
              "topic_id": "1ecb",
          },
          {
              "coref_chain": "HUM16236184328979740",
              "doc_id": "1_12ecb.xml",
              "mention_context": [
                  "Tara",
                  "Reid",
                  "has",
                  "checked",
                  "into",
                  "Promises",
                  "Treatment",
                  "Center",
                  ",",
                  "a",
                  "prominent",
                  "rehab",
                  "clinic",
                  "in",
                  "Los",
                  "Angeles",
                  ".",
              ],
              "mention_head": "Reid",
              "mention_head_lemma": "reid",
              "mention_head_pos": "PROPN",
              "mention_id": "1",
              "mention_index": -1,
              "mention_ner": "PERSON",
              "mention_type": "HUM",
              "predicted_coref_chain": None,
              "score": -1.0,
              "sent_id": 1,
              "tokens_number": [0, 1],
              "tokens_str": "Tara Reid",
              "topic_id": "1ecb",
          },
          {
              "coref_chain": "Singleton_LOC_8_1_12ecb",
              "doc_id": "1_12ecb.xml",
              "mention_context": [
                  "Tara",
                  "Reid",
                  "has",
                  "checked",
                  "into",
                  "Promises",
                  "Treatment",
                  "Center",
                  ",",
                  "a",
                  "prominent",
                  "rehab",
                  "clinic",
                  "in",
                  "Los",
                  "Angeles",
                  ".",
              ],
              "mention_head": "in",
              "mention_head_lemma": "in",
              "mention_head_pos": "ADP",
              "mention_id": "2",
              "mention_index": -1,
              "mention_ner": None,
              "mention_type": "LOC",
              "predicted_coref_chain": None,
              "score": -1.0,
              "sent_id": 1,
              "tokens_number": [13, 14, 15],
              "tokens_str": "in Los Angeles",
              "topic_id": "1ecb",
          },
          {
              "coref_chain": "HUM16236184328979740",
              "doc_id": "0",
              "mention_context": None,
              "mention_head": "Reid",
              "mention_head_lemma": "reid",
              "mention_head_pos": "PROPN",
              "mention_id": "3",
              "mention_ner": "PERSON",
              "mention_type": "HUM",
              "predicted_coref_chain": None,
              "score": -1.0,
              "sent_id": 0,
              "tokens_number": [3, 4],
              "tokens_str": "Tara Reid",
              "topic_id": "1ecb",
          },
      ]
  
      mentions = list()
      for json in mentions_json:
          mentions.append(MentionData.read_json_mention_data_line(json))
  
      return mentions
  
imports: from nlp_architect.common.cdc.mention_data import MentionData
label_desc: Use image node_id 1 for calling a global function with example usage: get_embedd_mentions() and returns: mentions