docstr: LSTMs take additional layer inputs h_0, c_0.
  
  Args:
      device: torch.device
      stats: dictionary from item names (e.g. input_tensor, module, labels)
      to their respective sizes
  
  Returns: updated stats dictionary with input tensor, h_0, and c_0 size
code: def _inputs_to(
      self, device: torch.device, stats: Dict[str, int]
  ) -> Dict[str, int]:
      """LSTMs take additional layer inputs h_0, c_0.
  
      Args:
          device: torch.device
          stats: dictionary from item names (e.g. input_tensor, module, labels)
          to their respective sizes
  
      Returns: updated stats dictionary with input tensor, h_0, and c_0 size
      """
      stats = super()._inputs_to(device=device, stats=stats)
      self._h_0 = self._h_0.to(device)
      stats["h_0"] = self._get_memory_difference(
          device=device, stats=stats
      )
  
      self._c_0 = self._c_0.to(device)
      stats["c_0"] = self._get_memory_difference(
          device=device, stats=stats
      )
  
      return stats
  
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 2 for calling the LSTMBase obj's underlying member method code with example usage: obj._inputs_to(device, stats) and returns: stats