class_docstr: Accumulates annotations by batches that correspond to objects detected on
  individual images. Can pack them together into single tensors.
class_docstr_tok: ["Accumulates","annotations","by","batches","that","correspond","to","objects","detected","on","individual","images",".","Can","pack","them","together","into","single","tensors","."]
code: def __init__(self):
      self.i_gt = []
      self.x_gt = []
      self.y_gt = []
      self.u_gt = []
      self.v_gt = []
      self.s_gt = []
      self.bbox_xywh_gt = []
      self.bbox_xywh_est = []
      self.point_bbox_with_dp_indices = []
      self.point_bbox_indices = []
      self.bbox_indices = []
      self.nxt_bbox_with_dp_index = 0
      self.nxt_bbox_index = 0
  
imports: from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  from torch.nn import functional
  from detectron2.structures import BoxMode, Instances
  from densepose import DensePoseDataRelative
label_desc: Use image node_id 1 to create a new ChartBasedAnnotationsAccumulator object from inherited base classes: AnnotationsAccumulator with example: obj = ChartBasedAnnotationsAccumulator()