code: def merge_class_box_level_outputs(
      params, cls_outputs: List[T], box_outputs: List[T]
  ) -> Tuple[T, T]:
      """Concatenates class and box of all levels into one tensor."""
      cls_outputs_all, box_outputs_all = [], []
      batch_size = tf.shape(cls_outputs[0])[0]
      for level in range(
          0, params["max_level"] - params["min_level"] + 1
      ):
          if params["data_format"] == "channels_first":
              cls_outputs[level] = tf.transpose(
                  cls_outputs[level], [0, 2, 3, 1]
              )
              box_outputs[level] = tf.transpose(
                  box_outputs[level], [0, 2, 3, 1]
              )
          cls_outputs_all.append(
              tf.reshape(
                  cls_outputs[level],
                  [batch_size, -1, params["num_classes"]],
              )
          )
          box_outputs_all.append(
              tf.reshape(box_outputs[level], [batch_size, -1, 4])
          )
      return tf.concat(cls_outputs_all, 1), tf.concat(
          box_outputs_all, 1
      )
  
file_path: DALI/docs/examples/use_cases/tensorflow/efficientdet/model/utils/postprocess.py
imports: from typing import List, Tuple
  from absl import logging
  import tensorflow
  from pipeline import anchors
label_desc: Use image node_id 3 for calling a global function with example usage: merge_class_box_level_outputs(params, cls_outputs, box_outputs) and returns: tf,  tf