docstr: :param model: gensim word2vec.Word2Vec model
  :param term_acceptance_re : SRE_Pattern, Regular expression to identify
      valid terms, default re.compile('[a-z]{3,}')
  :return: EmbeddingsResolver
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code: def embed_category(self, category, model=None):
      """
  
      :param model: gensim word2vec.Word2Vec model
      :param term_acceptance_re : SRE_Pattern, Regular expression to identify
          valid terms, default re.compile('[a-z]{3,}')
      :return: EmbeddingsResolver
      """
      self._verify_category(category)
      if self.term_acceptance_re is not None:
          acceptable_terms = set(
              [
                  t
                  for t in self.corpus_.get_terms()
                  if self.term_acceptance_re.match(t)
              ]
          )
      else:
          acceptable_terms = set(self.corpus_.get_terms())
      trained_model = CategorySpecificWord2VecFromParsedCorpus(
          self.corpus_, category, model
      ).train()
      self.category_word2vec_model_[category] = trained_model
      word2dwe = {
          word: trained_model.wv[word]
          for word in trained_model.wv.key_to_index.keys()
      }
      self.category_embeddings_[category] = word2dwe
      return self
  
imports: import itertools
  import re
  import warnings
  import numpy
  import pandas
  import scipy
  from sklearn.metrics.pairwise import cosine_similarity
  from scattertext.ParsedCorpus import ParsedCorpus
  from scattertext.representations.Word2VecFromParsedCorpus import Word2VecDefault
label_desc: Use image node_id 3 for calling the CategoryEmbeddingsResolver obj's underlying member method code with example usage: obj.embed_category(category, model) and returns: self