class_docstr: Reads data suitable for object detection.
  
  The object detection task needs the following information:
      - images.
      - ground truth, rectangular bounding boxes with associated labels.
  
  For implementing a subclass of object detection, one needs to implement
  the following methods:
      - __init__
      - get_total
      - get_classes
      - iterate
  
  Optionally:
      - pretty_name
  
  Additionally, must use the `_per_class_counter` variable to honor the max
  number of examples per class in an efficient way.
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docstr: Determine if we should skip the current image, based on the options
  used for the reader.
  
  Args:
      - image_id: String with id of image.
  
  Returns:
      bool: True if record should be skipped, False if not.
docstr_tok: ["Determine","if","we","should","skip","the","current","image",",","based","on","the","options","used","for","the","reader",".","Args",":","-","image_id",":","String","with","id","of","image",".","Returns",":","bool",":","True","if","record","should","be","skipped",",","False","if","not","."]
code: def _should_skip(self, image_id):
      """
      Determine if we should skip the current image, based on the options
      used for the reader.
  
      Args:
          - image_id: String with id of image.
  
      Returns:
          bool: True if record should be skipped, False if not.
      """
      if self._only_images is not None and image_id is not None:
          # Skip because the current image_id was not asked for.
          if image_id not in self._only_images:
              return True
  
      return False
  
imports: import abc
  import six
  import tensorflow
  from collections import Counter
  from luminoth.tools.dataset.readers import BaseReader
label_desc: Use image node_id 10 for calling the ObjectDetectionReader obj's underlying member method code with example usage: obj._should_skip(image_id) and returns: False, True