code: def impl_abstract(qualname, func=None, *, lib=None, _stacklevel=1):
      r"""Register an abstract implementation for this operator.
  
      An "abstract implementation" specifies the behavior of this operator on
      Tensors that carry no data. Given some input Tensors with certain properties
      (sizes/strides/storage_offset/device), it specifies what the properties of
      the output Tensors are.
  
      The abstract implementation has the same signature as the operator.
      It is run for both FakeTensors and meta tensors. To write an abstract
      implementation, assume that all Tensor inputs to the operator are
      regular CPU/CUDA/Meta tensors, but they do not have storage, and
      you are trying to return regular CPU/CUDA/Meta tensor(s) as output.
      The abstract implementation must consist of only PyTorch operations
      (and may not directly access the storage or data of any input or
      intermediate Tensors).
  
      This API may be used as a decorator (see examples).
  
      For a detailed guide on custom ops, please see
      https://docs.google.com/document/d/1W--T6wz8IY8fOI0Vm8BF44PdBgs283QvpelJZWieQWQ/edit
  
      Examples:
          >>> import torch
          >>> import numpy as np
          >>> from torch import Tensor
          >>>
          >>> # Example 1: an operator without data-dependent output shape
          >>> torch.library.define(
          >>>     "mylib::custom_linear",
          >>>     "(Tensor x, Tensor weight, Tensor bias) -> Tensor")
          >>>
          >>> @torch.library.impl_abstract("mylib::custom_linear")
          >>> def custom_linear_abstract(x, weight):
          >>>     assert x.dim() == 2
          >>>     assert weight.dim() == 2
          >>>     assert bias.dim() == 1
          >>>     assert x.shape[1] == weight.shape[1]
          >>>     assert weight.shape[0] == bias.shape[0]
          >>>     assert x.device == weight.device
          >>>
          >>>     return (x @ weight.t()) + bias
          >>>
          >>> # Example 2: an operator with data-dependent output shape
          >>> torch.library.define("mylib::custom_nonzero", "(Tensor x) -> Tensor")
          >>>
          >>> @torch.library.impl_abstract("mylib::custom_nonzero")
          >>> def custom_nonzero_abstract(x):
          >>>     # Number of nonzero-elements is data-dependent.
          >>>     # Since we cannot peek at the data in an abstract impl,
          >>>     # we use the ctx object to construct a new symint that
          >>>     # represents the data-dependent size.
          >>>     ctx = torch.library.get_ctx()
          >>>     nnz = ctx.new_dynamic_size()
          >>>     shape = [nnz, x.dim()]
          >>>     result = x.new_empty(shape, dtype=torch.int64)
          >>>     return result
          >>>
          >>> @torch.library.impl("mylib::custom_nonzero", "cpu")
          >>> def custom_nonzero_cpu(x):
          >>>     x_np = x.numpy()
          >>>     res = np.stack(np.nonzero(x_np), axis=1)
          >>>     return torch.tensor(res, device=x.device)
  
      """
      source = torch._library.utils.get_source(_stacklevel + 1)
      frame = sys._getframe(_stacklevel)
      caller_module = inspect.getmodule(frame)
      # Can be none if you call impl_abstract from somewhere there isn't a module
      # (e.g. __main__)
      caller_module_name = (
          None if caller_module is None else caller_module.__name__
      )
  
      # TODO(rzou): We're gonna need to stage this change with torchvision,
      # since torchvision is github first.
      if (
          caller_module_name is not None
          and caller_module_name.startswith("torchvision.")
      ):
          caller_module_name = None
  
      def inner(func):
          entry = torch._library.simple_registry.singleton.find(
              qualname
          )
          if caller_module_name is not None:
              func_to_register = _check_pystubs_once(
                  func, qualname, caller_module_name
              )
          else:
              func_to_register = func
  
          handle = entry.abstract_impl.register(
              func_to_register, source
          )
          if lib is not None:
              lib._registration_handles.append(handle)
          return func
  
      if func is None:
          return inner
      return inner(func)
  
imports: from ._ops import OpOverload
  from typing import Any, Optional, Set, List
  import traceback
  import torch
  import weakref
  import functools
  import inspect
  import re
  import sys
label_desc: Use image node_id 8 for calling a global function with example usage: impl_abstract(qualname, func) and returns: inner, func, inner