text: """
  Count Embedder Class
  """
  
  from sklearn.feature_extraction.text import CountVectorizer
  
  
  class CountEmbedder(object):
      """Count Embedder"""
  
      def __init__(self, stop_words='english', ngram_range=(1, 2),
                   strip_accents='unicode'):
          super(CountEmbedder, self).__init__()
  
          self.vectorizer = CountVectorizer(stop_words=stop_words,
                                            ngram_range=ngram_range,
                                            strip_accents=strip_accents)
  
      def train(self, input_data):
          """Compute inverse document frequencies
          """
  
          self.vectorizer.fit(input_data)
  
      def process(self, input_data):
          """Split the input text into an array of tokens, and replace each
          token with the unique identifier for that token in the spacy vocab
          """
  
          return self.vectorizer.transform(input_data)
  