code: def track_forward(self, *args, **kwargs):
      super().track_forward(*args, **kwargs)
      with torch.no_grad():
          model_device = next(
              iter(self.teacher_model.parameters())
          ).device
          args = tree_map(
              lambda x: x.to(model_device)
              if isinstance(x, torch.Tensor)
              else x,
              args,
          )
          kwargs = tree_map(
              lambda x: x.to(model_device)
              if isinstance(x, torch.Tensor)
              else x,
              kwargs,
          )
          self.teacher_model(*args, **kwargs)
  
imports: from __future__ import annotations
  from collections import defaultdict
  import logging
  from typing import Any, Callable, Dict, List, overload
  import torch
  import torch.nn.functional
  from torch.utils._pytree import tree_map
  from ..base.compressor import Compressor, Distiller, _DISTILLATION_TARGET_SPACES
  from ..base.wrapper import ModuleWrapper, register_wrappers
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 1 for calling the Adaptive1dLayerwiseDistiller obj's underlying member method code with example usage: obj.track_forward() without return types