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.
docstr: Generate responsible anchor flags of grid cells in multiple scales.
  
  Args:
      featmap_sizes (list(tuple)): List of feature map sizes in multiple
          feature levels.
      gt_bboxes (Tensor): Ground truth boxes, shape (n, 4).
      device (str): Device where the anchors will be put on.
  
  Return:
      list(torch.Tensor): responsible flags of anchors in multiple level
code: def responsible_flags(self, featmap_sizes, gt_bboxes, device="cuda"):
      """Generate responsible anchor flags of grid cells in multiple scales.
  
      Args:
          featmap_sizes (list(tuple)): List of feature map sizes in multiple
              feature levels.
          gt_bboxes (Tensor): Ground truth boxes, shape (n, 4).
          device (str): Device where the anchors will be put on.
  
      Return:
          list(torch.Tensor): responsible flags of anchors in multiple level
      """
      assert self.num_levels == len(featmap_sizes)
      multi_level_responsible_flags = []
      for i in range(self.num_levels):
          anchor_stride = self.strides[i]
          flags = self.single_level_responsible_flags(
              featmap_sizes[i],
              gt_bboxes,
              anchor_stride,
              self.num_base_anchors[i],
              device=device,
          )
          multi_level_responsible_flags.append(flags)
      return multi_level_responsible_flags
  
imports: import mmcv
  import numpy
  import torch
  from torch.nn.modules.utils import _pair
  from .builder import ANCHOR_GENERATORS
label_desc: Use image node_id 5 for calling the YOLOAnchorGenerator obj's underlying member method code with example usage: obj.responsible_flags(featmap_sizes, gt_bboxes, device) and returns: multi_level_responsible_flags