Randint — torch_randint • torch

Randint

Usage

torch_randint(
  low,
  high,
  size,
  generator = NULL,
  dtype = NULL,
  layout = NULL,
  device = NULL,
  requires_grad = FALSE,
  memory_format = torch_preserve_format()
)

Arguments

low: (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
high: (int) One above the highest integer to be drawn from the distribution.
size: (tuple) a tuple defining the shape of the output tensor.
generator: (torch.Generator, optional) a pseudorandom number generator for sampling
dtype: (torch.dtype, optional) the desired data type of returned tensor. Default: if NULL, uses a global default (see torch_set_default_tensor_type).
layout: (torch.layout, optional) the desired layout of returned Tensor. Default: torch_strided.
device: (torch.device, optional) the desired device of returned tensor. Default: if NULL, uses the current device for the default tensor type (see torch_set_default_tensor_type). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad: (bool, optional) If autograd should record operations on the returned tensor. Default: FALSE.
memory_format: memory format for the resulting tensor.

randint(low=0, high, size, *, generator=NULL, out=NULL, \

dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -> Tensor

Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive).

The shape of the tensor is defined by the variable argument size.

.. note: With the global dtype default (torch_float32), this function returns a tensor with dtype torch_int64.

Examples

if (torch_is_installed()) {

torch_randint(3, 5, list(3))
torch_randint(0, 10, size = list(2, 2))
torch_randint(3, 10, list(2, 2))
}
#> torch_tensor
#>  6  8
#>  5  7
#> [ CPUFloatType{2,2} ]