code: def impl(qualname, types, func=None, *, lib=None):
      """Register an implementation for a device type for this operator.
  
      You may pass "default" for ``types`` to register this implementation as the
      default implementation for ALL device types.
      Please only use this if the implementation truly supports all device types;
      for example, this is true if it is a composition of built-in PyTorch operators.
  
      Some valid types are: "cpu", "cuda", "xla", "mps", "ipu", "xpu".
  
      Args:
          qualname (str): Should be a string that looks like "namespace::operator_name".
          types (str | Sequence[str]): The device types to register an impl to.
          lib (Optional[Library]): If provided, the lifetime of this registration
              will be tied to the lifetime of the Library object.
  
      Examples:
          >>> import torch
          >>> import numpy as np
          >>>
          >>> # Define the operator
          >>> torch.library.define("mylibrary::sin", "(Tensor x) -> Tensor")
          >>>
          >>> # Add implementations for the cpu device
          >>> @torch.library.impl("mylibrary::sin", "cpu")
          >>> def f(x):
          >>>     return torch.from_numpy(np.sin(x.numpy()))
          >>>
          >>> x = torch.randn(3)
          >>> y = torch.ops.mylibrary.sin(x)
          >>> assert torch.allclose(y, x.sin())
      """
      if isinstance(types, str):
          types = (types,)
      keys = set({})
      for typ in types:
          is_dispatch_key = torch._C._parse_dispatch_key(typ)
          if is_dispatch_key:
              # We also support passing a DispatchKey to impl. Please prefer using
              # the higher-level torch.library APIs and only pass DispatchKey to
              # torch.library.impl with caution (or even better, don't use this
              # option and file an issue on GitHub for what you need).
              # We don't advertise this to users because
              # it is very easy to shoot yourself in the foot.
              keys.add(typ)
          else:
              keys.add(_device_type_to_key(typ))
  
      def register(func):
          namespace, _ = torch._library.utils.parse_namespace(qualname)
          if lib is None:
              use_lib = Library(namespace, "FRAGMENT")
              _keep_alive.append(use_lib)
          else:
              use_lib = lib
          for key in keys:
              use_lib.impl(qualname, func, key)
  
      if func is None:
          return register
      else:
          register(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 5 for calling a global function with example usage: impl(qualname, types, func) and returns: register