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supports_inplace_updates#

ivy.supports_inplace_updates(x, /)[source]#

Return if in-place operations are supported for x’s data type.

Determine whether in-place operations are supported for x’s data type, by the current backend framework setting.

Parameters:

x (Union[Array, NativeArray]) – Input variable for whose data type we check whether the current backend framework supports in-place operations.

Return type:

bool

Returns:

ret – Value depends on whether in-place operations are supported for data type of x.

Raises:

  • IvyException – If x isn’t a class instance of ivy.Array or ivy.NativeArray, an exception will be raised.

  • This function is nestable, and therefore also accepts :code:'ivy.Container' –

  • instance in place of the argument. –

Examples

With ivy.Array input and default backend set as numpy:

>>> x = ivy.array([0, 1, 2])
>>> y = ivy.supports_inplace_updates(x)
>>> print(y)
True

With ivy.Container input and backend set as torch:

>>> x = ivy.Container(a=ivy.array([5., 6.]), b=ivy.array([7., 8.]))
>>> y = ivy.supports_inplace_updates(x)
>>> print(y)
{
    a: True,
    b: True
}

With ivy.Array input and backend set as “tensorflow”:

>>> x = ivy.array([1., 4.2, 2.2])
>>> ret = x.supports_inplace_updates()
>>> print(ret)
False
Array.supports_inplace_updates(self, /)[source]#

ivy.Array instance method variant of ivy.supports_inplace_updates. This method simply wraps the function, and so the docstring for ivy.supports_inplace_updates also applies to this method with minimal changes.

Parameters:

self (Array) – The input array whose elements’ data type is to be checked.

Return type:

bool

Returns:

ret – Bool value depends on whether the currently active backend framework supports in-place operations with argument’s data type.

Examples

With ivy.Array input and default backend set as numpy:

>>> x = ivy.array([0, 1, 2])
>>> ret = x.supports_inplace_updates()
>>> print(ret)
True

With ivy.Array input and backend set as “tensorflow”:

>>> x = ivy.array([1., 4.2, 2.2])
>>> ret = x.supports_inplace_updates()
>>> print(ret)
False
Container.supports_inplace_updates(self, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

ivy.Container instance method variant of ivy.supports_inplace_updates. This method simply wraps the static function, and so the docstring for the static variant also applies to this method with minimal changes.

Parameters:

  • self (Container) – An ivy.Container whose elements are data types supported by Ivy.

  • key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.

  • to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.

  • prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.

  • map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

Return type:

Container

Returns:

ret – An ivy.Container instance of bool values. True if nodes of the Container support in-place operations. False otherwise.

Examples

With ivy.Container input and backend set as torch:

>>> x = ivy.Container(a=ivy.array([5., 6.]), b=ivy.array([7., 8.]))
>>> ret = x.supports_inplace_updates()
>>> print(ret)
{
    a: True,
    b: True
}

With ivy.Container input and backend set as jax:

>>> x = ivy.Container(a=ivy.array([5.]), b=ivy.array([7.]))
>>> ret = x.supports_inplace_updates()
>>> print(ret)
{
    a: False,
    b: False
}
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