docstr:     The private calculation method for decoder.
  
  Parameters
  ----------
  tgt_seq: torch.Tensor,
      The target sequence of shape :math:`(B, seq_len)`, where :math:`B`
      is size of batch, :math:`seq_len` is the length of sequences.
      Note that it is consisted by tokens' index.
  graph_node_embedding: torch.Tensor,
      The graph node embedding matrix of shape :math:`(B, N, D_{in})`
  graph_node_mask: torch.Tensor,
      The graph node type mask matrix of shape :math`(B, N)`
  rnn_node_embedding: torch.Tensor,
      The rnn encoded embedding matrix of shape :math`(B, N, D_{in})`
  graph_level_embedding: torch.Tensor,
      graph level embedding of shape :math`(B, D_{in})`
  graph_edge_embedding: torch.Tensor,
      graph edge embedding of shape :math`(B, N, D_{in})`
  graph_edge_mask: torch.Tensor,
      graph edge type embedding
  
  Returns
  ----------
  logits: torch.Tensor
  attns: torch.Tensor
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code: def _run_forward_pass(
      self,
      graph_node_embedding,
      graph_node_mask,
      rnn_node_embedding,
      graph_level_embedding,
      graph_edge_embedding=None,
      graph_edge_mask=None,
      tgt_seq=None,
  ):
      r"""
          The private calculation method for decoder.
  
      Parameters
      ----------
      tgt_seq: torch.Tensor,
          The target sequence of shape :math:`(B, seq_len)`, where :math:`B`
          is size of batch, :math:`seq_len` is the length of sequences.
          Note that it is consisted by tokens' index.
      graph_node_embedding: torch.Tensor,
          The graph node embedding matrix of shape :math:`(B, N, D_{in})`
      graph_node_mask: torch.Tensor,
          The graph node type mask matrix of shape :math`(B, N)`
      rnn_node_embedding: torch.Tensor,
          The rnn encoded embedding matrix of shape :math`(B, N, D_{in})`
      graph_level_embedding: torch.Tensor,
          graph level embedding of shape :math`(B, D_{in})`
      graph_edge_embedding: torch.Tensor,
          graph edge embedding of shape :math`(B, N, D_{in})`
      graph_edge_mask: torch.Tensor,
          graph edge type embedding
  
      Returns
      ----------
      logits: torch.Tensor
      attns: torch.Tensor
      """
      raise NotImplementedError()
  
imports: import torch.nn
label_desc: Use image node_id 5 for calling the RNNDecoderBase obj's underlying member method code with example usage: obj._run_forward_pass(graph_node_embedding, graph_node_mask, rnn_node_embedding, graph_level_embedding, graph_edge_embedding, graph_edge_mask, tgt_seq) without return types