class_docstr: EfficientDet keras network with train and test step.
code: def call(self, inputs, training):
      config = self.config
      # call backbone network.
      all_feats = self.backbone(
          inputs, training=training, features_only=True
      )
      feats = all_feats[config.min_level : config.max_level + 1]
  
      # Build additional input features that are not from backbone.
      for resample_layer in self.resample_layers:
          feats.append(resample_layer(feats[-1], training, None))
  
      # call feature network.
      fpn_feats = self.fpn_cells(feats, training)
  
      # call class/box output network.
      class_outputs = self.class_net(fpn_feats, training)
      box_outputs = self.box_net(fpn_feats, training)
      return (class_outputs, box_outputs)
  
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 7 for calling the EfficientDetNet obj's underlying member method code with example usage: obj.call(inputs, training) and returns: class_outputs,  box_outputs