mindspore.mint.randint_like

mindspore.mint.randint_like(input, low=0, high, *, dtype=None, device=None) → Tensor[source]

Returns a new tensor filled with integer numbers from the uniform distribution over an interval \([low, high)\) based on the given dtype and shape of the input tensor.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

input (Tensor) – Input Tensor to specify the output shape and its default dtype.
low (int, optional) – the lower bound of the generated random number. Default: 0.
high (int) – the upper bound of the generated random number

Keyword Arguments

dtype (mindspore.dtype, optional) – Designated tensor dtype. If None, the same dtype of input will be applied. Default: None .
device (str, optional) – The specified device of the output tensor. Only "Ascend" and "npu" are supported. If device = None, the device of input will be used. Default: None .

Returns

Tensor, with the designated shape and dtype, filled with random numbers from the uniform distribution on the interval \([low, high)\).

Raises

TypeError – If low or high is not integer.
RuntimeError – If Input device is "CPU", and device is None .
RuntimeError – If device is "CPU" .
ValueError – If device is "GPU" .

Supported Platforms:: Ascend

Examples

>>> import mindspore as ms
>>> from mindspore import Tensor, mint
>>> a = Tensor([[2, 3, 4], [1, 2, 3]]).to('Ascend')
>>> low = 0
>>> high = 5
>>> print(mint.randint_like(a, low, high, dtype=ms.int32).shape)
(2, 3)