code: def __init__(
      self,
      num_vocabs,
      x_size,
      h_size,
      num_classes,
      dropout,
      pretrained_emb=None,
  ):
      super(TreeLSTM, self).__init__()
      self.x_size = x_size
      self.embedding = nn.Embedding(num_vocabs, x_size)
      if pretrained_emb is not None:
          print("Using glove")
          self.embedding.weight.data.copy_(pretrained_emb)
          self.embedding.weight.requires_grad = True
      self.dropout = nn.Dropout(dropout)
      self.linear = nn.Linear(h_size, num_classes)
      self.cell = TreeLSTMCell(x_size, h_size)
  
imports: import os
  from collections import namedtuple
  import dgl
  from dgl.data.tree import SSTDataset
  import matplotlib.pyplot
  import networkx
  import torch
  import torch.nn
  import dgl.function
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new TreeLSTM object from inherited base classes: nn with example: obj = TreeLSTM(num_vocabs, x_size, h_size, num_classes, dropout, pretrained_emb)