mindspore.mint.mul

mindspore.mint.mul(input, other) → Tensor[source]

Multiplies the other value by the input Tensor.

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

Note

When the two inputs have different shapes, they must be able to broadcast to a common shape.
The two inputs comply with the implicit type conversion rules to make the data types consistent.

Parameters:

input (Union[Tensor, number.Number, bool]) – The first input is a number.Number or a bool or a tensor whose data type is number or bool.
other (Union[Tensor, number.Number, bool]) –
The second input, is a number.Number or a bool or a tensor whose data type is number or bool.

Returns:

Tensor, with shape that is the same as the broadcasted shape of input and other, and with data type that has the higher precision of the two inputs.

Raises:

TypeError – If the data types of input and other are not one of the following: Tensor, number.Number, bool.

Supported Platforms:: Ascend

Examples

>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import mint
>>> x = Tensor(np.array([2, 6, 9]).astype(np.int32))
>>> y = Tensor(np.array([4, 5, 6]).astype(np.float32))
>>> output = mint.mul(x, y)
>>> print(output)
[8. 30. 54.]
>>> # the data type of x is int32, the data type of y is float32,
>>> # and the output is the data format of higher precision float32.
>>> print(output.dtype)
Float32