code: def __init__(self, hidden_size, output_size, attention_type, dropout):
      super(AttnUnit, self).__init__()
      self.hidden_size = hidden_size
      self.separate_attention = attention_type != "uniform"
  
      if self.separate_attention == "separate_different_encoder_type":
          self.linear_att = nn.Linear(
              3 * self.hidden_size, self.hidden_size
          )
      else:
          self.linear_att = nn.Linear(
              2 * self.hidden_size, self.hidden_size
          )
  
      self.linear_out = nn.Linear(self.hidden_size, output_size)
      self.dropout = nn.Dropout(dropout)
  
      self.softmax = nn.Softmax(dim=1)
      self.logsoftmax = nn.LogSoftmax(dim=1)
  
imports: import random
  import time
  import warnings
  import numpy
  import torch
  import torch.nn.functional
  import torch.nn.init
  from torch import nn, optim
  from ....modules.prediction.generation.TreeBasedDecoder import StdTreeDecoder, create_mask
  from ....modules.utils.tree_utils import DataLoader, Tree, to_cuda
label_desc: Use image node_id 1 to create a new AttnUnit object from inherited base classes: nn with example: obj = AttnUnit(hidden_size, output_size, attention_type, dropout)