class_docstr: Input reader for dataset.
class_docstr_tok: ["Input","reader","for","dataset","."]
code: def process_example(
      self,
      batch_size,
      images,
      cls_targets,
      box_targets,
      num_positives,
      boxes,
      classes,
  ):
      params = self._params
  
      """Processes one batch of data."""
      if params["data_format"] == "channels_first":
          images = tf.transpose(images, [0, 3, 1, 2])
  
      data = [images, num_positives, boxes, tf.cast(classes, tf.int32)]
      for level in range(params["min_level"], params["max_level"] + 1):
          cls = cls_targets[level]
          box = box_targets[level]
          if params["data_format"] == "channels_first":
              cls = tf.transpose(cls, [0, 3, 1, 2])
              box = tf.transpose(box, [0, 3, 1, 2])
          data.append(cls)
          data.append(box)
  
      return data
  
imports: from absl import logging
  import tensorflow
  import utils
  from pipeline import anchors
  from .None import preprocessor
  from .None import tf_example_decoder
label_desc: Use image node_id 3 for calling the InputReader obj's underlying member method code with example usage: obj.process_example(batch_size, images, cls_targets, box_targets, num_positives, boxes, classes) and returns: data