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Use this to guard a call to :func:`handle_torch_function`; don't use it to test if something is Tensor-like, use :func:`is_tensor_like` instead. Arguments --------- relevant_args : iterable Iterable or arguments to check for __torch_function__ methods. Returns ------- bool True if any of the elements of relevant_args have __torch_function__ implementations, False otherwise. See Also ________ torch.is_tensor_like Checks if something is a Tensor-like, including an exact ``Tensor``. z�Special case of `has_torch_function` for single inputs. Instead of: `has_torch_function((t,))` call: `has_torch_function_unary(t)` which skips unnecessary packing and unpacking work. a'Special case of `has_torch_function` that skips tuple creation. 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Returns ------- str Name of the function; if eval'ed it should give back the input function. r)r<r �_opsZ OpOverloadZOpOverloadPacket�strrD�get)�fr/r/r0rosrcCst�}t|tj�}|S)z<Returns a set of the overridable methods on ``torch.Tensor``)rrr r3)rB�methodsr/r/r0�_get_tensor_methods�srJcCs|t�vp |jdkS)aw Returns True if the function passed in is a handler for a method or property belonging to ``torch.Tensor``, as passed into ``__torch_function__``. .. note:: For properties, their ``__get__`` method must be passed in. This may be needed, in particular, for the following reasons: 1. Methods/properties sometimes don't contain a `__module__` slot. 2. They require that the first passed-in argument is an instance of ``torch.Tensor``. Examples -------- >>> is_tensor_method_or_property(torch.Tensor.add) True >>> is_tensor_method_or_property(torch.add) False r�)rJrrr/r/r0r�srcCst|�tjup t|d�S)a9 Returns ``True`` if the passed-in input is a Tensor-like. Currently, this occurs whenever there's a ``__torch_function__`` attribute on the type of the input. Examples -------- A subclass of tensor is generally a Tensor-like. >>> class SubTensor(torch.Tensor): ... >>> is_tensor_like(SubTensor([0])) True Built-in or user types aren't usually Tensor-like. >>> is_tensor_like(6) False >>> is_tensor_like(None) False >>> class NotATensor: ... >>> is_tensor_like(NotATensor()) False But, they can be made Tensor-like by implementing __torch_function__. >>> class TensorLike: ... @classmethod ... def __torch_function__(cls, func, types, args, kwargs): ... return -1 >>> is_tensor_like(TensorLike()) True r�)r�r r3r)�inpr/r/r0r�s"rc@sJeZdZUdZded<ddd�Zddd �Zd d �Zd d �Ze dd��Z dS)�TorchFunctionModea A ``TorchFunctionMode`` allows you to override the meaning of all ``__torch_function__`` overrideable functions within a dynamic scope, without having to actually create a tensor subclass or manually monkey-patch functions in the PyTorch API. Some common situations where you should use a mode: * You want to override the meaning of factory functions, or other functions that do not otherwise take a tensor as an argument (these cannot be overridden with tensor subclasses). * You want to override the behavior of all functions without needing to wrap your inputs in tensor subclasses; e.g., if you are just interested in logging intermediate computations. * You want to control the order of execution of various tensor subclasses explicitly, rather than implicitly via the return of ``NotImplemented``. Independent subclasses of :class:`TorchFunctionMode` are compositional: modes can be pushed onto a stack using ``with MyMode():``. When you call functions in the PyTorch API inside your ``__torch_function__`` implementation, by default, they will forward on to the next mode on the mode stack. 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