code: def __init__(
      self,
      input_size,
      enc_emb_size,
      enc_hidden_size,
      dec_hidden_size,
      dropout_input,
      dropout_output,
      pad_idx=0,
      encode_rnn_num_layer=1,
      embeddings=None,
  ):
      super(SequenceEncoder, self).__init__()
      if embeddings is None:
          self.embedding = nn.Embedding(
              input_size, enc_emb_size, padding_idx=pad_idx
          )
      else:
          self.embedding = embeddings
  
      self.rnn = nn.LSTM(
          enc_emb_size,
          enc_hidden_size,
          encode_rnn_num_layer,
          bias=True,
          batch_first=True,
          dropout=dropout_output,
          bidirectional=True,
      )
      self.fc = nn.Linear(enc_hidden_size * 4, dec_hidden_size)
      self.dropout = None
      if dropout_input > 0:
          self.dropout = nn.Dropout(dropout_input)
  
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 SequenceEncoder object from inherited base classes: nn with example: obj = SequenceEncoder(input_size, enc_emb_size, enc_hidden_size, dec_hidden_size, dropout_input, dropout_output, pad_idx, encode_rnn_num_layer, embeddings)