code: def register_backend_specific(backend, backend_func):
      """A decorator to create a backend specific function.
  
      Parameters
      ----------
      backend : str
          String that specifies which backend implementation to overload. Must be an entry
          in the ``tensorly.available_backend_names``-list.
      backend_func : callable
          Function implementation for the given backend. The function signature must match
          the signature of the backend agnostic implementation exactly (including which
          arguments are keyword-/positional-only and the default arguments) to ensure that
          the documentation is correct.
  
      Returns
      -------
      func
          Decorator that creates a backend specific function
  
      Examples
      --------
      >>> from tensorly.backend import backend_context
      >>> from tensorly.utils.backend_specific import register_backend_specific
      >>> def _numpy_func(x):
      ...    return 2
      >>> @register_backend_specific("numpy", _numpy_func)
      ... def func(x):
      ...     return x
      >>> with backend_context("numpy"):
      ...     print(func(5))
      2
      >>> with backend_context("pytorch"):
      ...     print(func(5))
      5
      """
      if backend not in available_backend_names:
          raise ValueError(
              f"Unknown backend name '{backend}', "
              + f"known backends are {available_backend_names}"
          )
  
      def wrapper(f):
          if getfullargspec(f) != getfullargspec(backend_func):
              raise TypeError(
                  "Signature of backend spesific implementation does not match the "
                  + "signature of the backend agnostic implementation. All arguments and "
                  + "default values must match, otherwise the documentation will be"
                  + "incorrect."
              )
  
          @wraps(f)
          def wrapped(*args, **kwargs):
              if get_backend() == backend:
                  return backend_func(*args, **kwargs)
              return f(*args, **kwargs)
  
          return wrapped
  
      return wrapper
  
imports: from functools import wraps
  from ..None import get_backend, available_backend_names
  from inspect import getfullargspec
label_desc: Use image node_id 1 for calling a global function with example usage: register_backend_specific(backend, backend_func) and returns: wrapper, wrapped, f, backend_func