docstr:     One step for decoding
  Parameters
  ----------
  decoder_input: torch.Tensor
      The input for current decoding step
  rnn_state: torch.Tensor
      Rnn_state
  encoder_out: torch.Tensor
      The graph node embedding for decoding
  dec_input_mask: torch.Tensor
      The mask of graph node.
      Notes: ``-1`` is the dummy node, each int larger than -1 is one class for separate attention. # noqa
  rnn_emb: torch.Tensor
      The graph node embedding from RNN encoder.
  enc_attn_weights_average: list
      The list of encoder attention weights. It will be used for coverage.
  src_seq: torch.Tensor
      The source sequence. It will be used for copy.
  oov_dict: Vocab
      The vocabulary containing out-of-vocabulary words.
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code: def decode_step(
      self,
      decoder_input,
      rnn_state,
      encoder_out,
      dec_input_mask,
      rnn_emb=None,
      enc_attn_weights_average=None,
      src_seq=None,
      oov_dict=None,
  ):
      r"""
          One step for decoding
      Parameters
      ----------
      decoder_input: torch.Tensor
          The input for current decoding step
      rnn_state: torch.Tensor
          Rnn_state
      encoder_out: torch.Tensor
          The graph node embedding for decoding
      dec_input_mask: torch.Tensor
          The mask of graph node.
          Notes: ``-1`` is the dummy node, each int larger than -1 is one class for separate attention. # noqa
      rnn_emb: torch.Tensor
          The graph node embedding from RNN encoder.
      enc_attn_weights_average: list
          The list of encoder attention weights. It will be used for coverage.
      src_seq: torch.Tensor
          The source sequence. It will be used for copy.
      oov_dict: Vocab
          The vocabulary containing out-of-vocabulary words.
      """
      raise NotImplementedError()
  
imports: import torch.nn
label_desc: Use image node_id 6 for calling the RNNDecoderBase obj's underlying member method code with example usage: obj.decode_step(decoder_input, rnn_state, encoder_out, dec_input_mask, rnn_emb, enc_attn_weights_average, src_seq, oov_dict) without return types