mindspore.mint.div

mindspore.mint.div(input, other, *, rounding_mode=None) → Tensor[source]

Divides each element of the input by the corresponding element of the other .

\[out_{i} = input_{i} / other_{i}\]

Note

When the two inputs have different shapes, they must be able to broadcast to a common shape.
The two inputs cannot be bool type at the same time, [True, Tensor(True), Tensor(np.array([True]))] are all considered bool type.
The two inputs comply with the implicit type conversion rules to make the data types consistent.

Parameters:

input (Union[Tensor, Number, bool]) – The dividend.
other (Union[Tensor, Number, bool]) – The divisor.

Keyword Arguments:

rounding_mode (str, optional) –

Type of rounding applied to the result. Default: None . Three types are defined as,

None: Default behavior, which is the same as true division in Python or true_divide in NumPy.
"floor": Rounds the division of the inputs down, which is the same as floor division in Python or floor_divide in NumPy.
"trunc": Rounds the division of the inputs towards zero, which is the same as C-style integer division.

Returns:

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision or higher digits among the two inputs.

Raises:

TypeError – If input or other is not one of the following: Tensor, Number, bool.
ValueError – If rounding_mode value is not None, "floor" or "trunc".

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32)
>>> output = mint.div(x, y)
>>> print(output)
[0.25 0.4 0.5]