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# torch.randint

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`.

  Note

  With the global dtype default (`torch.float32`), this function returns a tensor with dtype `torch.int64`.

  Parameters:

    - 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) – if `None`, this function returns a tensor with dtype `torch.int64`.

    - 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`.

  Example:

      >>> 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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