botorch/utils/sampling.py [232:256]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    d: int,
    n: int = 1,
    qmc: bool = False,
    seed: Optional[int] = None,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
) -> Tensor:
    r"""Sample uniformly from a unit d-sphere.

    Args:
        d: The dimension of the hypersphere.
        n: The number of samples to return.
        qmc: If True, use QMC Sobol sampling (instead of i.i.d. uniform).
        seed: If provided, use as a seed for the RNG.
        device: The torch device.
        dtype:  The torch dtype.

    Returns:
        An  `n x d` tensor of uniform samples from from the d-hypersphere.

    Example:
        >>> sample_hypersphere(d=5, n=10)
    """
    dtype = torch.float if dtype is None else dtype
    if d == 1:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



botorch/utils/sampling.py [271:295]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    d: int,
    n: int = 1,
    qmc: bool = False,
    seed: Optional[int] = None,
    device: Optional[torch.device] = None,
    dtype: Optional[torch.dtype] = None,
) -> Tensor:
    r"""Sample uniformly from a d-simplex.

    Args:
        d: The dimension of the simplex.
        n: The number of samples to return.
        qmc: If True, use QMC Sobol sampling (instead of i.i.d. uniform).
        seed: If provided, use as a seed for the RNG.
        device: The torch device.
        dtype: The torch dtype.

    Returns:
        An `n x d` tensor of uniform samples from from the d-simplex.

    Example:
        >>> sample_simplex(d=3, n=10)
    """
    dtype = torch.float if dtype is None else dtype
    if d == 1:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -