def random_intrinsics(low: Union[float, Tensor], high: Union[float, Tensor]) -> Tensor:
    r"""Generate a random camera matrix based on a given uniform distribution.

    Args:
        low: lower range (inclusive).
        high: upper range (exclusive).

    Returns:
        the random camera matrix with the shape of :math:`(1, 3, 3)`.

    """
    sampler = torch.distributions.Uniform(low, high)
    fx, fy, cx, cy = (sampler.sample(torch.Size((1,))) for _ in range(4))
    zeros, ones = zeros_like(fx), ones_like(fx)
    camera_matrix = concatenate([fx, zeros, cx, zeros, fy, cy, zeros, zeros, ones])
    return camera_matrix.view(1, 3, 3)
Generate a random camera matrix based on a given uniform distribution.

    Args:
        low: lower range (inclusive).
        high: upper range (exclusive).

    Returns:
        the random camera matrix with the shape of :math:`(1, 3, 3)`.

    
kornia/geometry/epipolar/projection.py