class_docstr: XML dataset for detection.
  
  Args:
      min_size (int | float, optional): The minimum size of bounding
          boxes in the images. If the size of a bounding box is less than
          ``min_size``, it would be add to ignored field.
docstr: Get category ids in XML file by index.
  
  Args:
      idx (int): Index of data.
  
  Returns:
      list[int]: All categories in the image of specified index.
docstr_tok: ["Get","category","ids","in","XML","file","by","index",".","Args",":","idx","(","int",")",":","Index","of","data",".","Returns",":","list","[","int","]",":","All","categories","in","the","image","of","specified","index","."]
code: def get_cat_ids(self, idx):
      """Get category ids in XML file by index.
  
      Args:
          idx (int): Index of data.
  
      Returns:
          list[int]: All categories in the image of specified index.
      """
  
      cat_ids = []
      img_id = self.data_infos[idx]["id"]
      xml_path = osp.join(
          self.img_prefix, "Annotations", f"{img_id}.xml"
      )
      tree = ET.parse(xml_path)
      root = tree.getroot()
      for obj in root.findall("object"):
          name = obj.find("name").text
          if name not in self.CLASSES:
              continue
          label = self.cat2label[name]
          cat_ids.append(label)
  
      return cat_ids
  
file_path: insightface/detection/scrfd/mmdet/datasets/xml_style.py
imports: import os.path
  import xml.etree.ElementTree
  import mmcv
  import numpy
  from PIL import Image
  from .builder import DATASETS
  from .custom import CustomDataset
label_desc: Use image node_id 5 for calling the XMLDataset obj's underlying member method code with example usage: obj.get_cat_ids(idx) and returns: cat_ids