class_docstr: Initialize `nn.Module` with empty tensors, i.e., uninitialized memory.
  
  Example::
  
      with _EmptyInit():
          model = BigModel()
      model.load_state_dict(torch.load("checkpoint.pt"))
code: def __torch_function__(
      self,
      func: Callable,
      types: Sequence,
      args: Sequence[Any] = (),
      kwargs: Optional[Dict] = None,
  ) -> Any:
      kwargs = kwargs or {}
      if not self.enabled:
          return func(*args, **kwargs)
      if getattr(func, "__module__", None) == "torch.nn.init":
          if "tensor" in kwargs:
              return kwargs["tensor"]
          return args[0]
      return func(*args, **kwargs)
  
file_path: pytorch-lightning/src/lightning/fabric/utilities/init.py
imports: import itertools
  from typing import Any, Callable, Dict, Optional, Sequence
  import torch
  from typing_extensions import override
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_1_13, _TORCH_GREATER_EQUAL_2_1
  from lightning.fabric.utilities.types import _DEVICE
label_desc: Use image node_id 2 for calling the _EmptyInit obj's underlying member method code with example usage: obj.__torch_function__(func, types, args, kwargs) and returns: func, func, args, kwargs