code: def __init__(
      self,
      *,
      layer: Union[Type[nn.LSTM], Type[DPLSTM]],
      batch_size: int,
      seq_len: int,
      input_size: int,
      hidden_size: int,
      num_layers: int = 1,
      bias: bool = False,
      batch_first: bool = False,
      dropout: float = 0,
      bidirectional: bool = False,
      proj_size: int = 0,
      random_seed: Optional[int] = None,
      criterion: Callable = F.cross_entropy,
  ) -> None:
      super().__init__(
          layer=layer,
          batch_size=batch_size,
          seq_len=seq_len,
          input_size=input_size,
          hidden_size=hidden_size,
          num_layers=num_layers,
          bias=bias,
          batch_first=batch_first,
          dropout=dropout,
          bidirectional=bidirectional,
          proj_size=proj_size,
          random_seed=random_seed,
          criterion=criterion,
      )
      h_out = proj_size if proj_size > 0 else hidden_size
      D = 2 if bidirectional else 1
      self._h_0 = torch.randn(D * num_layers, batch_size, h_out)
      self._c_0 = torch.randn(D * num_layers, batch_size, hidden_size)
  
      self._labels = (
          torch.randn(seq_len, batch_size, D * h_out)
          if not batch_first
          else torch.randn(batch_size, seq_len, D * h_out)
      )
  
imports: from enum import Enum
  from typing import Callable, Dict, Optional, Tuple, Type, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from opacus.grad_sample import GradSampleModule
  from opacus.grad_sample.utils import wrap_model
  from opacus.layers import DPGRU, DPLSTM, DPRNN, DPMultiheadAttention
  from opacus.layers.dp_rnn import DPRNNBase
label_desc: Use image node_id 1 to create a new LSTMBase object from inherited base classes: RNNBase with example: obj = LSTMBase()