code: def direct_update_preprocess(
      self, model_speedup: "ModelSpeedup", node: Node
  ):
      super().direct_update_preprocess(model_speedup, node)
  
      with torch.no_grad():
          node_info: "NodeInfo" = model_speedup.node_infos[node]
          for k, v in node_info.module.named_parameters():
              randomize_tensor_inplace(v)
              v *= node_info.param_masks[k]
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Tuple
  import operator
  import torch
  from torch.nn import functional
  from torch.fx.node import Node
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from .utils import randomize_tensor_inplace, randomize_if_tensor, tree_map_zip, torch_float_dtype, poss_deepcopy
label_desc: Use image node_id 2 for calling the LeafModuleMaskUpdater obj's underlying member method code with example usage: obj.direct_update_preprocess(model_speedup, node) without return types