class_docstr: Detection dataset loaded from record file.
  The supported record file is using the same format used by
  :py:meth:`mxnet.image.ImageDetIter` and :py:meth:`mxnet.io.ImageDetRecordIter`.
  
  Checkout :ref:`lst_record_dataset` for tutorial of how to prepare this file.
  
  .. note::
  
      We suggest you to use ``RecordFileDetection`` only if you are familiar with
      the record files.
  
  Parameters
  ----------
  filename : str
      Path of the record file. It require both *.rec and *.idx file in the same
      directory, where raw image and labels are stored in *.rec file for better
      IO performance, *.idx file is used to provide random access to the binary file.
  coord_normalized : boolean
      Indicate whether bounding box coordinates have been normalized to (0, 1) in labels.
      If so, we will rescale back to absolute coordinates by multiplying width or height.
  
  Examples
  --------
  >>> record_dataset = RecordFileDetection('train.rec')
  >>> img, label = record_dataset[0]
  >>> print(img.shape, label.shape)
  (512, 512, 3) (1, 5)
code: def __getitem__(self, idx):
      img, label = super(RecordFileDetection, self).__getitem__(idx)
      h, w, _ = img.shape
      if self._coord_normalized:
          label = _transform_label(label, h, w)
      else:
          label = _transform_label(label)
      return img, label
  
file_path: gluon-cv/gluoncv/data/recordio/detection.py
imports: from __future__ import absolute_import
  from __future__ import division
  import numpy
  from mxnet import gluon
label_desc: Use image node_id 2 for calling the RecordFileDetection obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: img,  label