multiply#

ivy.multiply(x1, x2, /, *, out=None)[source]#

Calculate the product for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

Parameters:

  • x1 (Union[float, Array, NativeArray]) – first input array. Should have a numeric data type.

  • x2 (Union[float, Array, NativeArray]) – second input array. Must be compatible with x1 (see :ref’broadcasting). Should have a numeric data type

  • out (Optional[Array]) – optional output array, for writing the array result to. (default: None) It must have a shape that the inputs broadcast to.

  • Standard (This function conforms to the `Array API) –

  • <https (`docstring) –

  • <https –

  • standard. (in the) –

  • simplicity (Both the description and the type hints above assumes an array input for) –

:param : :param but this function is nestable: :param and therefore also accepts ivy.Container: :param instances in place of any of the arguments.:

Return type:

Array

Returns:

ret – an array containing the element-wise products. The returned array must have a data type determined by Type Promotion Rules.

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([3., 5., 7.])
>>> x2 = ivy.array([4., 6., 8.])
>>> y = ivy.multiply(x1, x2)
>>> print(y)
ivy.array([12., 30., 56.])

With ivy.NativeArray inputs:

>>> x1 = ivy.native_array([1., 3., 9.])
>>> x2 = ivy.native_array([4., 7.2, 1.])
>>> y = ivy.multiply(x1, x2)
>>> print(y)
ivy.array([ 4. , 21.6,  9. ])

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([8., 6., 7.])
>>> x2 = ivy.native_array([1., 2., 3.])
>>> y = ivy.multiply(x1, x2)
>>> print(y)
ivy.array([ 8., 12., 21.])

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([12.,4.,6.]), b=ivy.array([3.,1.,5.]))
>>> x2 = ivy.Container(a=ivy.array([1.,3.,4.]), b=ivy.array([3.,3.,2.]))
>>> y = ivy.multiply(x1, x2)
>>> print(y)
{
    a: ivy.array([12.,12.,24.]),
    b: ivy.array([9.,3.,10.])
}

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([3., 4., 5.]), b=ivy.array([2., 2., 1.]))
>>> x2 = ivy.array([1.,2.,3.])
>>> y = ivy.multiply(x1, x2)
>>> print(y)
{
    a: ivy.array([3.,8.,15.]),
    b: ivy.array([2.,4.,3.])
}
Array.multiply(self, x2, /, *, out=None)#

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

Parameters:

  • self (Array) – first input array. Should have a real-valued data type.

  • x2 (Union[Array, NativeArray]) – second input array. Must be compatible with the first input array. (see broadcasting). Should have a real-valued data type.

  • out (Optional[Array]) – optional output array, for writing the result to. It must have a shape that (default: None) the inputs broadcast to.

Return type:

Array

Returns:

ret – an array containing the element-wise products. The returned array must have a data type determined by type-promotion.

Examples

With ivy.Array inputs:

>>> x1 = ivy.array([3., 5., 7.])
>>> x2 = ivy.array([4., 6., 8.])
>>> y = x1.multiply(x2)
>>> print(y)
ivy.array([12., 30., 56.])

With mixed ivy.Array and ivy.NativeArray inputs:

>>> x1 = ivy.array([8., 6., 7.])
>>> x2 = ivy.native_array([1., 2., 3.])
>>> y = x1.multiply(x2)
>>> print(y)
ivy.array([ 8., 12., 21.])
Container.multiply(self, x2, /, *, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)#

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

Parameters:

  • self (Container) – first input array or container. Should have a numeric data type.

  • x2 (Union[Container, Array, NativeArray]) – second input array or container. Must be compatible with self (see broadcasting). Should have a nuneric data type.

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

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

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

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

  • out (Optional[Container]) – optional output container, for writing the result to. It must have a shape (default: None) that the inputs broadcast to.

Return type:

Container

Returns:

ret – a container containing the element-wise products. The returned container must have a data type determined by type-promotion.

Examples

With ivy.Container inputs:

>>> x1 = ivy.Container(a=ivy.array([15., 4.5, 6.5]),                               b=ivy.array([3.2, 5., 7.5]))
>>> x2 = ivy.Container(a=ivy.array([1.7, 2.8, 3.]),                               b=ivy.array([5.6, 1.2, 4.2]))
>>> y = ivy.Container.multiply(x1, x2)
>>> print(y)
{
    a: ivy.array([25.5, 12.6, 19.5]),
    b: ivy.array([17.9, 6., 31.5])
}

With mixed ivy.Container and ivy.Array inputs:

>>> x1 = ivy.Container(a=ivy.array([6.2, 4.8, 2.3]),                               b=ivy.array([5., 1.7, 0.1]))
>>> x2 = ivy.array([8.3, 3.2, 6.5])
>>> y = ivy.Container.multiply(x1, x2)
>>> print(y)
{
    a: ivy.array([51.5, 15.4, 14.9]),
    b: ivy.array([41.5, 5.44, 0.65])
}