def check_points_inside_bboxes(points, bboxes, eps=1e-09):
    '\n    Args:\n        points (Tensor, float32): shape[L, 2], "xy" format, L: num_anchors\n        bboxes (Tensor, float32): shape[B, n, 4], "xmin, ymin, xmax, ymax" format\n        eps (float): Default: 1e-9\n    Returns:\n        is_in_bboxes (Tensor, float32): shape[B, n, L], value=1. means selected\n    '
    points = points.unsqueeze([0, 1])
    (x, y) = points.chunk(2, axis=(- 1))
    (xmin, ymin, xmax, ymax) = bboxes.unsqueeze(2).chunk(4, axis=(- 1))
    l = (x - xmin)
    t = (y - ymin)
    r = (xmax - x)
    b = (ymax - y)
    bbox_ltrb = paddle.concat([l, t, r, b], axis=(- 1))
    return (bbox_ltrb.min(axis=(- 1)) > eps).astype(bboxes.dtype)
Args:
    points (Tensor, float32): shape[L, 2], "xy" format, L: num_anchors
    bboxes (Tensor, float32): shape[B, n, 4], "xmin, ymin, xmax, ymax" format
    eps (float): Default: 1e-9
Returns:
    is_in_bboxes (Tensor, float32): shape[B, n, L], value=1. means selected
def check_points_inside_bboxes(points, bboxes, eps=1e-09):
    '\n    Args:\n        points (Tensor, float32): shape[L, 2], "xy" format, L: num_anchors\n        bboxes (Tensor, float32): shape[B, n, 4], "xmin, ymin, xmax, ymax" format\n        eps (float): Default: 1e-9\n    Returns:\n        is_in_bboxes (Tensor, float32): shape[B, n, L], value=1. means selected\n    '
    points = points.unsqueeze([0, 1])
    (x, y) = points.chunk(2, axis=(- 1))
    (xmin, ymin, xmax, ymax) = bboxes.unsqueeze(2).chunk(4, axis=(- 1))
    l = (x - xmin)
    t = (y - ymin)
    r = (xmax - x)
    b = (ymax - y)
    bbox_ltrb = paddle.concat([l, t, r, b], axis=(- 1))
    return (bbox_ltrb.min(axis=(- 1)) > eps).astype(bboxes.dtype)