def draw_rectangle(
    image: torch.Tensor, rectangle: torch.Tensor, color: Optional[torch.Tensor] = None, fill: Optional[bool] = None
) -> torch.Tensor:
    r"""Draw N rectangles on a batch of image tensors.

    Args:
        image: is tensor of BxCxHxW.
        rectangle: represents number of rectangles to draw in BxNx4
            N is the number of boxes to draw per batch index[x1, y1, x2, y2]
            4 is in (top_left.x, top_left.y, bot_right.x, bot_right.y).
        color: a size 1, size 3, BxNx1, or BxNx3 tensor.
            If C is 3, and color is 1 channel it will be broadcasted.
        fill: is a flag used to fill the boxes with color if True.

    Returns:
        This operation modifies image inplace but also returns the drawn tensor for
        convenience with same shape the of the input BxCxHxW.

    Example:
        >>> img = torch.rand(2, 3, 10, 12)
        >>> rect = torch.tensor([[[0, 0, 4, 4]], [[4, 4, 10, 10]]])
        >>> out = draw_rectangle(img, rect)

    """
    batch, c, h, w = image.shape
    batch_rect, num_rectangle, num_points = rectangle.shape
    if batch != batch_rect:
        raise AssertionError("Image batch and rectangle batch must be equal")
    if num_points != 4:
        raise AssertionError("Number of points in rectangle must be 4")

    # clone rectangle, in case it's been expanded assignment from clipping causes problems
    rectangle = rectangle.long().clone()

    # clip rectangle to hxw bounds
    rectangle[:, :, 1::2] = torch.clamp(rectangle[:, :, 1::2], 0, h - 1)
    rectangle[:, :, ::2] = torch.clamp(rectangle[:, :, ::2], 0, w - 1)

    if color is None:
        color = torch.tensor([0.0] * c).expand(batch, num_rectangle, c)

    if fill is None:
        fill = False

    if len(color.shape) == 1:
        color = color.expand(batch, num_rectangle, c)
    b, n, color_channels = color.shape

    if color_channels == 1 and c == 3:
        color = color.expand(batch, num_rectangle, c)

    for b in range(batch):
        for n in range(num_rectangle):
            if fill:
                image[
                    b,
                    :,
                    int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1),
                    int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1),
                ] = color[b, n, :, None, None]
            else:
                image[b, :, int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1), rectangle[b, n, 0]] = color[
                    b, n, :, None
                ]
                image[b, :, int(rectangle[b, n, 1]) : int(rectangle[b, n, 3] + 1), rectangle[b, n, 2]] = color[
                    b, n, :, None
                ]
                image[b, :, rectangle[b, n, 1], int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1)] = color[
                    b, n, :, None
                ]
                image[b, :, rectangle[b, n, 3], int(rectangle[b, n, 0]) : int(rectangle[b, n, 2] + 1)] = color[
                    b, n, :, None
                ]

    return image
Draw N rectangles on a batch of image tensors.

    Args:
        image: is tensor of BxCxHxW.
        rectangle: represents number of rectangles to draw in BxNx4
            N is the number of boxes to draw per batch index[x1, y1, x2, y2]
            4 is in (top_left.x, top_left.y, bot_right.x, bot_right.y).
        color: a size 1, size 3, BxNx1, or BxNx3 tensor.
            If C is 3, and color is 1 channel it will be broadcasted.
        fill: is a flag used to fill the boxes with color if True.

    Returns:
        This operation modifies image inplace but also returns the drawn tensor for
        convenience with same shape the of the input BxCxHxW.

    Example:
        >>> img = torch.rand(2, 3, 10, 12)
        >>> rect = torch.tensor([[[0, 0, 4, 4]], [[4, 4, 10, 10]]])
        >>> out = draw_rectangle(img, rect)

    
kornia/utils/draw.py