class_docstr: Standard points generator for multi-level (Mlvl) feature maps in 2D
  points-based detectors.
  
  Args:
      strides (list[int] | list[tuple[int, int]]): Strides of anchors
          in multiple feature levels in order (w, h).
      offset (float): The offset of points, the value is normalized with
          corresponding stride. Defaults to 0.5.
class_docstr_tok: ["Standard","points","generator","for","multi-level","(","Mlvl",")","feature","maps","in","2D","points-based","detectors",".","Args",":","strides","(","list","[","int","]","|","list","[","tuple","[","int",",","int","]","]",")",":","Strides","of","anchors","in","multiple","feature","levels","in","order","(","w",",","h",")",".","offset","(","float",")",":","The","offset","of","points",",","the","value","is","normalized","with","corresponding","stride",".","Defaults","to","0.5","."]
docstr: list[int]: The number of priors (points) at a point
  on the feature grid
docstr_tok: ["list","[","int","]",":","The","number","of","priors","(","points",")","at","a","point","on","the","feature","grid"]
code: def num_base_priors(self) -> List[int]:
      """list[int]: The number of priors (points) at a point
      on the feature grid"""
      return [1 for _ in range(len(self.strides))]
  
file_path: mmdetection/mmdet/models/task_modules/prior_generators/point_generator.py
imports: from typing import List, Tuple, Union
  import numpy
  import torch
  from torch import Tensor
  from torch.nn.modules.utils import _pair
  from mmdet.registry import TASK_UTILS
label_desc: Use image node_id 3 for calling the MlvlPointGenerator obj's underlying member method code with example usage: obj.num_base_priors() and returns: unknown