code: def forward(self, input_src, prev_c, prev_h, parent_h, sibling_state):
      src_emb = self.embedding(input_src)
      src_emb = self.dropout(src_emb)
      if self.use_sibling:
          input_single_step = torch.cat(
              (src_emb, parent_h, sibling_state), 1
          )
      else:
          input_single_step = torch.cat((src_emb, parent_h), 1)
      prev_cy, prev_hy = self.lstm(input_single_step, (prev_c, prev_h))
      return prev_cy, prev_hy, input_single_step
  
imports: import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from graph4nlp.pytorch.modules.utils.tree_utils import Tree, to_cuda
  from .attention import Attention
  from .base import RNNTreeDecoderBase
label_desc: Use image node_id 2 for calling the TreeDecodingUnit obj's underlying member method code with example usage: obj.forward(input_src, prev_c, prev_h, parent_h, sibling_state) and returns: prev_cy,  prev_hy,  input_single_step