lucid.random.randint¶

lucid.random.randint(low: int, high: int | None, size: int | list[int] | tuple[int] = 1, requires_grad: bool = False, keep_grad: bool = False, device: Literal['cpu', 'gpu'] = 'cpu') → Tensor¶

The randint function generates a tensor of the specified shape, filled with random integer values from a specified range.

Function Signature¶

def randint(
    low: int,
    high: int | None,
    size: int | _ShapeLike,
    requires_grad: bool = False,
    keep_grad: bool = False,
    device: _DeviceType = "cpu",
) -> Tensor

Parameters¶

low (int): The lowest integer (inclusive) in the range from which random values will be drawn.
high (int, optional): The highest integer (exclusive) in the range. If None, the random values will be drawn from the range \([0, \text{low})\).
size (int or ShapeLike): The shape of the output tensor.
requires_grad (bool, optional): If True, the resulting tensor will track gradients for automatic differentiation. Defaults to False.
keep_grad (bool, optional): Determines whether gradient history should persist across multiple operations. Defaults to False.

Returns¶

Tensor: A tensor of shape size filled with random integers drawn from the specified range.

Example¶

>>> import lucid
>>> x = lucid.random.randint(0, 10, (2, 3))
>>> print(x)
Tensor([[9, 7, 3],
        [7, 8, 2]], grad=None)

By default, the generated tensor does not track gradients. Set requires_grad=True to enable gradient tracking:

>>> y = lucid.random.randint(1, 5, (3, 2), requires_grad=True)
>>> print(y.requires_grad)
True

Note

The random values are drawn from a discrete uniform distribution.
Use lucid.random.seed to ensure reproducibility of random values.
The gradient calculation for this operation is not defined because the output is discrete.