def bbox_generator(
    x_start: torch.Tensor, y_start: torch.Tensor, width: torch.Tensor, height: torch.Tensor
) -> torch.Tensor:
    """Generate 2D bounding boxes according to the provided start coords, width and height.

    Args:
        x_start: a tensor containing the x coordinates of the bounding boxes to be extracted. Shape must be a scalar
            tensor or :math:`(B,)`.
        y_start: a tensor containing the y coordinates of the bounding boxes to be extracted. Shape must be a scalar
            tensor or :math:`(B,)`.
        width: widths of the masked image. Shape must be a scalar tensor or :math:`(B,)`.
        height: heights of the masked image. Shape must be a scalar tensor or :math:`(B,)`.

    Returns:
        the bounding box tensor.

    Examples:
        >>> x_start = torch.tensor([0, 1])
        >>> y_start = torch.tensor([1, 0])
        >>> width = torch.tensor([5, 3])
        >>> height = torch.tensor([7, 4])
        >>> bbox_generator(x_start, y_start, width, height)
        tensor([[[0, 1],
                 [4, 1],
                 [4, 7],
                 [0, 7]],
        <BLANKLINE>
                [[1, 0],
                 [3, 0],
                 [3, 3],
                 [1, 3]]])

    """
    if not (x_start.shape == y_start.shape and x_start.dim() in [0, 1]):
        raise AssertionError(f"`x_start` and `y_start` must be a scalar or (B,). Got {x_start}, {y_start}.")
    if not (width.shape == height.shape and width.dim() in [0, 1]):
        raise AssertionError(f"`width` and `height` must be a scalar or (B,). Got {width}, {height}.")
    if not x_start.dtype == y_start.dtype == width.dtype == height.dtype:
        raise AssertionError(
            "All tensors must be in the same dtype. Got "
            f"`x_start`({x_start.dtype}), `y_start`({x_start.dtype}), `width`({width.dtype}), `height`({height.dtype})."
        )
    if not x_start.device == y_start.device == width.device == height.device:
        raise AssertionError(
            "All tensors must be in the same device. Got "
            f"`x_start`({x_start.device}), `y_start`({x_start.device}), "
            f"`width`({width.device}), `height`({height.device})."
        )

    bbox = torch.tensor([[[0, 0], [0, 0], [0, 0], [0, 0]]], device=x_start.device, dtype=x_start.dtype).repeat(
        1 if x_start.dim() == 0 else len(x_start), 1, 1
    )

    bbox[:, :, 0] += x_start.view(-1, 1)
    bbox[:, :, 1] += y_start.view(-1, 1)
    bbox[:, 1, 0] += width - 1
    bbox[:, 2, 0] += width - 1
    bbox[:, 2, 1] += height - 1
    bbox[:, 3, 1] += height - 1

    return bbox
Generate 2D bounding boxes according to the provided start coords, width and height.

    Args:
        x_start: a tensor containing the x coordinates of the bounding boxes to be extracted. Shape must be a scalar
            tensor or :math:`(B,)`.
        y_start: a tensor containing the y coordinates of the bounding boxes to be extracted. Shape must be a scalar
            tensor or :math:`(B,)`.
        width: widths of the masked image. Shape must be a scalar tensor or :math:`(B,)`.
        height: heights of the masked image. Shape must be a scalar tensor or :math:`(B,)`.

    Returns:
        the bounding box tensor.

    Examples:
        >>> x_start = torch.tensor([0, 1])
        >>> y_start = torch.tensor([1, 0])
        >>> width = torch.tensor([5, 3])
        >>> height = torch.tensor([7, 4])
        >>> bbox_generator(x_start, y_start, width, height)
        tensor([[[0, 1],
                 [4, 1],
                 [4, 7],
                 [0, 7]],
        <BLANKLINE>
                [[1, 0],
                 [3, 0],
                 [3, 3],
                 [1, 3]]])

    
kornia/geometry/bbox.py