class_docstr: Compute words embedding features for mentions
code: def __init__(self, pretrained_model_path):
      (
          _,
          self.static_embeddings,
          self.stat_idx,
          self.stat_voc,
      ) = self.load_embeddings_from_file(
          pretrained_model_path + "static_word"
      )
      (
          _,
          self.tuned_embeddings,
          self.tun_idx,
          self.tun_voc,
      ) = self.load_embeddings_from_file(
          pretrained_model_path + "tuned_word"
      )
      self.fallback = self.static_embeddings.get(UNKNOWN_WORD)
  
      self.shape = self.static_embeddings[UNKNOWN_WORD].shape
      shape2 = self.tuned_embeddings[UNKNOWN_WORD].shape
      assert self.shape == shape2
  
imports: import re
  import io
  from six import string_types, integer_types
  from spacy.tokens import Span, Token
  from neuralcoref.train.compat import unicode_
  from neuralcoref.train.utils import encode_distance, parallel_process
  import spacy
  import numpy
label_desc: Use image node_id 1 to create a new EmbeddingExtractor object  with example: obj = EmbeddingExtractor(pretrained_model_path)