class_docstr: The attribute summarizer for the RNN model.
  
  Args:
      word_contextualizer (string or :class:`~deepmatcher.WordContextualizer` or callable): The
          word contextualizer module (refer to :class:`~deepmatcher.WordContextualizer` for
          details) to use for attribute summarization. This model uses RNN to take into
          account the context information, and the default value is 'gru' (i.e., uses
          the bidirectional GRU model as the specific RNN instantiation.) Other options
          are 'rnn' (the vanilla bi-RNN) and 'lstm' (the bi-LSTM model).
      word_comparator (string or :class:`~deepmatcher.WordComparator` or callable): The word
          comparator module (refer to :class:`~deepmatcher.WordComparator` for details) to use
          for attribute summarization. The RNN model does not perform word by word
          comparisons, hence this defaults to None.
      word_aggregator (string or :class:`~deepmatcher.WordAggregator` or callable): The word
          aggregator module (refer to :class:`~deepmatcher.WordAggregator` for details) to use
          for attribute summarization. The RNN model uses bi-directional RNN and
          concatenates the last ouputs of the forward and backward RNNs, hence the
          default value is 'birnn-last-pool'.
      hidden_size (int): The hidden size to use for the word contextualizer. This value
          will also be used as the hidden size for the other 2 attribute summarization
          sub-modules (i.e., word comparator, and word aggregator), if they are
          customized. If not specified, the hidden size for each component will be set
          to be the same as its input size. E.g. if the word embedding dimension is 300
          and hidden_size is None, the word contextualizer's hidden size will be 300.
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code: def _init(
      self,
      word_contextualizer=None,
      word_comparator=None,
      word_aggregator=None,
      hidden_size=None,
  ):
      word_contextualizer = word_contextualizer or "gru"
      word_aggregator = word_aggregator or "birnn-last-pool"
      super(RNN, self)._init(
          word_contextualizer=word_contextualizer,
          word_comparator=word_comparator,
          word_aggregator=word_aggregator,
          hidden_size=hidden_size,
      )
  
file_path: deepmatcher/deepmatcher/models/attr_summarizers.py
imports: import deepmatcher
label_desc: Use image node_id 1 for calling the RNN obj's underlying member method code with example usage: obj._init(word_contextualizer, word_comparator, word_aggregator, hidden_size) without return types