class_docstr: Transforms tuples of text into tuples of vector sequences.
docstr: Initializes the vectorizer.
  
  Args:
      tokenizer_encoder: Tokenizer that encodes the input text.
      tokenizer_decoder: Tokenizer that encodes the target text.
      max_input_len (optional): Maximum length of input sequence,
          longer sequences will be truncated.
      max_output_len (optional): Maximum length of target sequence,
          longer sequences will be truncated and shorter sequences
          will be padded to max len.
code: def __init__(
      self,
      tokenizer_encoder: Tokenizer,
      tokenizer_decoder: Tokenizer,
      max_input_len=None,
      max_output_len=None,
  ) -> None:
      """
      Initializes the vectorizer.
  
      Args:
          tokenizer_encoder: Tokenizer that encodes the input text.
          tokenizer_decoder: Tokenizer that encodes the target text.
          max_input_len (optional): Maximum length of input sequence,
              longer sequences will be truncated.
          max_output_len (optional): Maximum length of target sequence,
              longer sequences will be truncated and shorter sequences
              will be padded to max len.
      """
      self.encoding_dim = tokenizer_encoder.vocab_size + 1
      self.decoding_dim = tokenizer_decoder.vocab_size + 1
      self.max_input_len = max_input_len
      self.max_output_len = max_output_len
      self._tokenizer_encoder = tokenizer_encoder
      self._tokenizer_decoder = tokenizer_decoder
  
file_path: headliner/headliner/preprocessing/vectorizer.py
imports: from typing import Tuple, List
  from headliner.preprocessing.tokenizer import Tokenizer
label_desc: Use image node_id 1 to create a new Vectorizer object  with example: obj = Vectorizer(tokenizer_encoder, tokenizer_decoder, max_input_len, max_output_len)