docstr: Moves input_tensor, additional inputs, module, and labels to device.
  
  Args:
      device: torch.device
  
  Returns:
      Dictionary from item names (e.g. input_tensor, module, labels) to
      their respective sizes if CUDA device, else respective sizes are all
      set to 0.
code: def to(self, device: torch.device) -> Dict[str, int]:
      """Moves input_tensor, additional inputs, module, and labels to device.
  
      Args:
          device: torch.device
  
      Returns:
          Dictionary from item names (e.g. input_tensor, module, labels) to
          their respective sizes if CUDA device, else respective sizes are all
          set to 0.
      """
      stats: Dict[str, int] = {}
      stats["offset"] = self._get_memory_difference(
          device=device, stats=stats
      )
  
      # some modules take additional inputs such as hidden state
      stats = self._inputs_to(device=device, stats=stats)
  
      self._module = self._module.to(device)
      stats["layer"] = self._get_memory_difference(
          device=device, stats=stats
      )
  
      self._labels = self._labels.to(device)
      stats["labels"] = self._get_memory_difference(
          device=device, stats=stats
      )
  
      # check that all memory is accounted for
      if device.type == "cuda":
          assert torch.cuda.memory_allocated(device) == sum(
              stats.values()
          )
  
      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 4 for calling the Layer obj's underlying member method code with example usage: obj.to(device) and returns: stats