class_docstr: Image iterator with a large number of augmentation choices for detection.
  
  Parameters
  ----------
  aug_list : list or None
      Augmenter list for generating distorted images
  batch_size : int
      Number of examples per batch.
  data_shape : tuple
      Data shape in (channels, height, width) format.
      For now, only RGB image with 3 channels is supported.
  path_imgrec : str
      Path to image record file (.rec).
      Created with tools/im2rec.py or bin/im2rec.
  path_imglist : str
      Path to image list (.lst).
      Created with tools/im2rec.py or with custom script.
      Format: Tab separated record of index, one or more labels and relative_path_from_root.
  imglist: list
      A list of images with the label(s).
      Each item is a list [imagelabel: float or list of float, imgpath].
  path_root : str
      Root folder of image files.
  path_imgidx : str
      Path to image index file. Needed for partition and shuffling when using .rec source.
  shuffle : bool
      Whether to shuffle all images at the start of each iteration or not.
      Can be slow for HDD.
  part_index : int
      Partition index.
  num_parts : int
      Total number of partitions.
  data_name : str
      Data name for provided symbols.
  label_name : str
      Name for detection labels
  last_batch_handle : str, optional
      How to handle the last batch.
      This parameter can be 'pad'(default), 'discard' or 'roll_over'.
      If 'pad', the last batch will be padded with data starting from the begining
      If 'discard', the last batch will be discarded
      If 'roll_over', the remaining elements will be rolled over to the next iteration
  kwargs : ...
      More arguments for creating augmenter. See mx.image.CreateDetAugmenter.
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docstr: Helper function to estimate label shape
docstr_tok: ["Helper","function","to","estimate","label","shape"]
code: def _estimate_label_shape(self):
      """Helper function to estimate label shape"""
      max_count, label = 0, None
      self.reset()
      try:
          while True:
              label, _ = self.next_sample()
              label = self._parse_label(label)
              max_count = max(max_count, label.shape[0])
      except StopIteration:
          pass
      self.reset()
      return (max_count, label.shape[1] if label is not None else 5)
  
imports: import json
  import logging
  import random
  import warnings
  import numpy
  from ..base import numeric_types
  from ..None import ndarray
  from ..ndarray._internal import _cvcopyMakeBorder
  from ..None import io
  from .image import RandomOrderAug, ColorJitterAug, LightingAug, ColorNormalizeAug
  from .image import ResizeAug, ForceResizeAug, CastAug, HueJitterAug, RandomGrayAug
  from .image import fixed_crop, ImageIter, Augmenter
  from ..util import is_np_array
  from ..None import numpy
label_desc: Use image node_id 3 for calling the ImageDetIter obj's underlying member method code with example usage: obj._estimate_label_shape() and returns: max_count,  unknown