mindspore.mint.mul

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

Multiply other value by input Tensor.

o u t_{i} = i n p u t_{i} \times o t h e r_{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, 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 the type of input, other is 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