torch.randint(low=0, high, size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, 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.

  • 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.

Keyword Arguments
  • generator (torch.Generator, optional) – a pseudorandom number generator for sampling

  • out (Tensor, optional) – the output tensor.

  • dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, 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 None, 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.


>>> torch.randint(3, 5, (3,))
tensor([4, 3, 4])

>>> torch.randint(10, (2, 2))
tensor([[0, 2],
        [5, 5]])

>>> torch.randint(3, 10, (2, 2))
tensor([[4, 5],
        [6, 7]])


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