code: def converter_hasheddoc(strings):
      # create string features
      f = sg.create_string_features(strings, sg.RAWBYTE, sg.PT_CHAR)
  
      # set the number of bits of the target dimension
      # means a dim of size 2^5=32
      num_bits = 5
  
      # create the ngram tokenizer of size 8 to parse the strings
      tokenizer = sg.NGramTokenizer(8)
  
      # normalize results
      normalize = True
  
      # create converter
      converter = sg.create_transformer(
          "HashedDocConverter",
          tokenizer=tokenizer,
          num_bits=num_bits,
          should_normalize=normalize,
      )
  
      converted_feats = converter.transform(f)
  
      # should expect 32
      # print('Converted features\' space dimensionality is', converted_feats.get_dim_feature_space())
  
      # print('Self dot product of string 0 with converted feats:', converted_feats.dot(0, converted_feats, 0))
  
      hashed_feats = sg.create_features(
          "HashedDocDotFeatures",
          num_bits=num_bits,
          doc_collection=f,
          tokenizer=tokenizer,
          should_normalize=normalize,
      )
  
      # print('Hashed features\' space dimensionality is', hashed_feats.get_dim_feature_space())
  
      # print('Self dot product of string 0 with hashed feats:', hashed_feats.dot(0, hashed_feats, 0))
  
      return converted_feats
  
label_desc: Use image node_id 1 for calling a global function with example usage: converter_hasheddoc(strings) and returns: converted_feats