class_docstr: EfficientDet keras network with train and test step.
code: def _unpack_inputs(self, inputs):
      config = self.config
      features, num_pos, _, _, *targets = inputs
  
      labels = {}
      for level in range(config.min_level, config.max_level + 1):
          i = 2 * (level - config.min_level)
          labels["cls_targets_%d" % level] = targets[i]
          labels["box_targets_%d" % level] = targets[i + 1]
      labels["mean_num_positives"] = tf.reshape(
          tf.tile(
              tf.expand_dims(tf.reduce_mean(num_pos), 0),
              [config.batch_size],
          ),
          [config.batch_size, 1],
      )
  
      return features, labels
  
imports: import functools
  from absl import logging
  import tensorflow
  import hparams_config
  import utils
  from .backbone import efficientnet_builder
  from .utils import postprocess
  from .utils import layers
  from .utils import losses
  import re
label_desc: Use image node_id 4 for calling the EfficientDetNet obj's underlying member method code with example usage: obj._unpack_inputs(inputs) and returns: features,  labels