code: def indirect_update_process(
      self, model_speedup: "ModelSpeedup", node: Node
  ):
      node_info = model_speedup.node_infos[node]
      batch_dim, batch_size = (
          model_speedup.batch_dim,
          model_speedup.batch_size,
      )
      node_info.output_masks = tree_map_zip(
          model_speedup.indirect_calc_mask,
          node_info.output_grad,
          node_info.output_masks,
      )
  
      def randomize_inputs(node_args):
          # output_origin -> randomize -> mask -> require_grad
          args = tree_map_zip(
              lambda nd: model_speedup.node_infos[nd].output_origin
              if isinstance(nd, Node)
              else nd,
              node_args,
          )
          args = tree_map_zip(
              lambda t: randomize_if_tensor(t, batch_dim, batch_size),
              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,
          )
  
          def require_grad_(obj):
              if (
                  isinstance(obj, torch.Tensor)
                  and model_speedup.tensor_propagate_check(obj)
                  and obj.dtype in torch_float_dtype
              ):
                  obj.requires_grad_(True)
              return obj
  
          return tree_map_zip(lambda t: require_grad_(t), args)
  
      # init apply input
      args = randomize_inputs(node.args)
      kwargs = randomize_inputs(node.kwargs)
  
      # Some operator may have the in_place operations, so we need to clone the input
      # before passing to the model_speedup.module
      args_cloned = tree_map_zip(
          lambda t: t.clone()
          if isinstance(t, torch.Tensor)
          else poss_deepcopy(t),
          args,
      )
      kwargs_cloned = tree_map_zip(
          lambda t: t.clone()
          if isinstance(t, torch.Tensor)
          else poss_deepcopy(t),
          kwargs,
      )
  
      output = getattr(model_speedup, node.op)(
          node.target, args_cloned, kwargs_cloned
      )
  
      tree_map_zip(
          model_speedup.indirect_backward,
          output,
          node_info.output_masks,
      )
  
      def indirect_pass_grad(nodes, args):
          if nodes is None:
              return
          elif isinstance(nodes, (list, tuple)):
              assert isinstance(args, (list, tuple))
              for x, y in zip(nodes, args):
                  indirect_pass_grad(x, y)
          elif isinstance(nodes, dict):
              assert isinstance(args, dict)
              for x, y in zip(nodes.values(), args.values()):
                  indirect_pass_grad(x, y)
          elif isinstance(nodes, Node):
              model_speedup.indirect_pass_grad(nodes, args)
          else:
              assert not isinstance(args, torch.Tensor)
  
      indirect_pass_grad(node.args, args)
      indirect_pass_grad(node.kwargs, kwargs)
  
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 6 for calling the DefaultMaskUpdater obj's underlying member method code with example usage: obj.indirect_update_process(model_speedup, node) and returns: tree_map_zip, obj