class_docstr: A sampler implementing Online Hard-negative mining.
  As described in paper https://arxiv.org/abs/1604.03540.
  
  Parameters
  ----------
  ratio : float
      Ratio of negative vs. positive samples. Values >= 1.0 is recommended.
  min_samples : int, default 0
      Minimum samples to be selected regardless of positive samples.
      For example, if positive samples is 0, we sometimes still want some num_negative
      samples to be selected.
  thresh : float, default 0.5
      IOU overlap threshold of selected negative samples. IOU must not exceed
      this threshold such that good matching anchors won't be selected as
      negative samples.
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code: def __init__(self, ratio, min_samples=0, thresh=0.5):
      super(OHEMSampler, self).__init__()
      assert ratio > 0, "OHEMSampler ratio must > 0, {} given".format(
          ratio
      )
      self._ratio = ratio
      self._min_samples = min_samples
      self._thresh = thresh
  
imports: from __future__ import absolute_import
  import numpy
  import mxnet
  from mxnet import gluon, nd
label_desc: Use image node_id 1 to create a new OHEMSampler object from inherited base classes: gluon with example: obj = OHEMSampler(ratio, min_samples, thresh)