docstr: the default MaskUpdater for leaf module, so return true if the node is a module calling
docstr_tok: ["the","default","MaskUpdater","for","leaf","module",",","so","return","true","if","the","node","is","a","module","calling"]
code: def detect(self, model_speedup: "ModelSpeedup", node: Node) -> bool:
      """
      the default MaskUpdater for leaf module, so return true if the node is a module calling
      """
      if node.op == "call_module":
          module: torch.nn.Module = model_speedup.fetch_attr(
              node.target
          )
          param_masks = model_speedup.masks.get(node.target, {})
          for k, v in module.named_parameters():
              if k not in param_masks:
                  param_masks[k] = torch.ones_like(v)
          model_speedup.node_infos[node].module = module
          model_speedup.node_infos[node].param_masks = param_masks
          return True
      else:
          return False
  
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 1 for calling the LeafModuleMaskUpdater obj's underlying member method code with example usage: obj.detect(model_speedup, node) and returns: True, False