class_docstr: Combines a list / sequence of embeddings into one using a biLSTM
class_docstr_tok: ["Combines","a","list","\/","sequence","of","embeddings","into","one","using","a","biLSTM"]
code: def __init__(self, lstm_dim: int):
      super().__init__()
      self.lstm_dim = lstm_dim
      self.lstm_fwd = nn.LSTM(lstm_dim, lstm_dim, num_layers=1)
      self.lstm_rev = nn.LSTM(lstm_dim, lstm_dim, num_layers=1)
      self.linear_seq = nn.Sequential(
          nn.Linear(2 * lstm_dim, lstm_dim), nn.Tanh()
      )
  
imports: from typing import Any, List, Sized, Tuple
  import torch
  import torch.nn
  from pytext.torchscript.utils import reverse_tensor_list
  from pytext.utils.cuda import FloatTensor
  from pytext.utils.tensor import xaviervar
label_desc: Use image node_id 1 to create a new CompositionalNN object from inherited base classes: torch.jit with example: obj = CompositionalNN(lstm_dim)