class_docstr: Anchor generator for YOLO.
  
  Args:
      strides (list[int] | list[tuple[int, int]]): Strides of anchors
          in multiple feature levels.
      base_sizes (list[list[tuple[int, int]]]): The basic sizes
          of anchors in multiple levels.
class_docstr_tok: ["Anchor","generator","for","YOLO",".","Args",":","strides","(","list","[","int","]","|","list","[","tuple","[","int",",","int","]","]",")",":","Strides","of","anchors","in","multiple","feature","levels",".","base_sizes","(","list","[","list","[","tuple","[","int",",","int","]","]","]",")",":","The","basic","sizes","of","anchors","in","multiple","levels","."]
docstr: int: number of feature levels that the generator will be applied
code: def num_levels(self) -> int:
      """int: number of feature levels that the generator will be applied"""
      return len(self.base_sizes)
  
imports: import warnings
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from mmengine.utils import is_tuple_of
  from torch import Tensor
  from torch.nn.modules.utils import _pair
  from mmdet.registry import TASK_UTILS
  from mmdet.structures.bbox import HorizontalBoxes
label_desc: Use image node_id 2 for calling the YOLOAnchorGenerator obj's underlying member method code with example usage: obj.num_levels() and returns: len