def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):
    """
    Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
    Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
    """
    # create a tensor of 'num' steps from 0 to 1
    steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)

    # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings
    # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript
    #   "cannot statically infer the expected size of a list in this contex", hence the code below
    for i in range(start.ndim):
        steps = steps.unsqueeze(-1)

    # the output starts at 'start' and increments until 'stop' in each dimension
    out = start[None] + steps * (stop - start)[None]

    return out

    Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
    Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
    
training/volumetric_rendering/math_utils.py