code: def __init__(
      self,
      input_size,
      emb_size,
      hidden_size,
      dropout_input,
      use_sibling,
      dec_embeddings,
  ):
      super(TreeDecodingUnit, self).__init__()
      self.hidden_size = hidden_size
      self.emb_size = emb_size
      self.embedding = dec_embeddings
      self.dropout = nn.Dropout(dropout_input)
  
      self.lstm = nn.LSTMCell(
          emb_size + hidden_size * (2 if use_sibling else 1),
          hidden_size,
      )
      self.use_sibling = use_sibling
  
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 1 to create a new TreeDecodingUnit object from inherited base classes: nn with example: obj = TreeDecodingUnit(input_size, emb_size, hidden_size, dropout_input, use_sibling, dec_embeddings)