docstr: Get all input from node_info.output_randomize and execute the node,
  calc the output_masks and store to node_info.output_masks
docstr_tok: ["Get","all","input","from","node_info.output_randomize","and","execute","the","node",",","calc","the","output_masks","and","store","to","node_info.output_masks"]
code: def direct_update_process(
      self, model_speedup: "ModelSpeedup", node: Node
  ):
      """
      Get all input from node_info.output_randomize and execute the node,
      calc the output_masks and store to node_info.output_masks
      """
      node_info = model_speedup.node_infos[node]
      with torch.no_grad():
          args = tree_map_zip(
              lambda nd: model_speedup.node_infos[nd].output_randomize
              if isinstance(nd, Node)
              else nd,
              node.args,
          )
          args_masks = tree_map_zip(
              lambda nd: model_speedup.node_infos[nd].output_masks
              if isinstance(nd, Node)
              else None,
              node.args,
          )
          args = tree_map_zip(
              lambda t, m: (t * m).type_as(t) if m is not None else t,
              args,
              args_masks,
          )
          kwargs = tree_map_zip(
              lambda nd: model_speedup.node_infos[nd].output_randomize
              if isinstance(nd, Node)
              else nd,
              node.kwargs,
          )
          kwargs_masks = tree_map_zip(
              lambda nd: model_speedup.node_infos[nd].output_masks
              if isinstance(nd, Node)
              else None,
              node.kwargs,
          )
          kwargs = tree_map_zip(
              lambda t, m: (t * m).type_as(t) if m is not None else t,
              kwargs,
              kwargs_masks,
          )
  
          output = getattr(model_speedup, node.op)(
              node.target, args, kwargs
          )
          if node_info.output_masks is not None:
              calc_masks = tree_map_zip(
                  model_speedup.direct_calc_mask,
                  output,
                  node_info.output_masks,
              )
          else:
              calc_masks = tree_map_zip(
                  model_speedup.direct_calc_mask, output
              )
  
          node_info.output_masks = calc_masks
  
      if model_speedup.garbage_collect_values:
          # do memory collect to reduce memory usage
          for to_delete in model_speedup.user_to_last_uses.get(
              node, []
          ):
              del model_speedup.node_infos[to_delete].output_randomize
  
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 3 for calling the DefaultMaskUpdater obj's underlying member method code with example usage: obj.direct_update_process(model_speedup, node) without return types