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Specifications and Common Mistakes

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mindspore.mint.remainder

mindspore.mint.remainder(input, other)

Computes the remainder of input divided by other element-wise. The result has the same sign as the divisor and its absolute value is less than that of other.

Supports broadcasting to a common shape and implicit type promotion.

$$remainder(input,other)=input-input.div(other,rounding\mathrm{\_}mode="floor")\ast other$$

Note

Complex inputs are not supported. At least one input need to be tensor, but not both are bool tensors.

Parameters

• input (Union[Tensor, numbers.Number, bool]) – The dividend is a numbers.Number or a bool or a tensor whose data type is number or bool_.

• other (Union[Tensor, numbers.Number, bool]) – The divisor is a numbers.Number or a bool or a tensor whose data type is number or bool_ when the dividend is a tensor. When the dividend is Scalar, the divisor must be a Tensor whose data type is number or bool_.

Returns

Tensor, with dtype promoted and shape broadcasted.

Raises

• TypeError – If input and other are not of types: (tensor, tensor), (tensor, number), (tensor, bool), (number, tensor) or (bool, tensor).

• ValueError – If input and other are not broadcastable.

Supported Platforms:

Ascend

Examples

>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.array([-4.0, 5.0, 6.0]).astype(np.float32))
>>> y = Tensor(np.array([3.0, 2.0, 3.0]).astype(np.float64))
>>> output = mint.remainder(x, y)
>>> print(output)
[2.  1.  0.]