code: def convert_numpy_data(data, metric):
      data_all = {}
      data_all = {k: np.array(v) for k, v in data.items()}
      if isinstance(metric, VOCMetric):
          for k, v in data_all.items():
              if not isinstance(v[0], np.ndarray):
                  tmp_list = []
                  for t in v:
                      tmp_list.append(np.array(t))
                  data_all[k] = np.array(tmp_list)
      else:
          data_all = {k: np.array(v) for k, v in data.items()}
      return data_all
  
file_path: PaddleDetection/deploy/auto_compression/run.py
imports: import os
  import sys
  import numpy
  import argparse
  import paddle
  from ppdet.core.workspace import load_config, merge_config
  from ppdet.core.workspace import create
  from ppdet.metrics import COCOMetric, VOCMetric, KeyPointTopDownCOCOEval
  from paddleslim.auto_compression.config_helpers import load_config
  from paddleslim.auto_compression import AutoCompression
  from post_process import PPYOLOEPostProcess
  from paddleslim.common.dataloader import get_feed_vars
label_desc: Use image node_id 3 for calling a global function with example usage: convert_numpy_data(data, metric) and returns: data_all