code: def __init__(
      self,
      num_vocabs,
      x_size,
      h_size,
      num_classes,
      dropout,
      cell_type="nary",
      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)
      cell = (
          TreeLSTMCell if cell_type == "nary" else ChildSumTreeLSTMCell
      )
      self.cell = cell(x_size, h_size)
  
imports: import itertools
  import time
  import dgl
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
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, cell_type, pretrained_emb)