code: def get_test_skeletons(input_file):
      assert input_file is not None, "--action_file can not be None"
      input_data = np.load(input_file)
      if input_data.ndim == 4:
          return [input_data]
      elif input_data.ndim == 5:
          output = list(
              map(
                  lambda x: np.squeeze(x, 0),
                  np.split(input_data, input_data.shape[0], 0),
              )
          )
          return output
      else:
          raise ValueError(
              "Now only support input with shape: (N, C, T, K, M) or (C, T, K, M)"
          )
  
imports: import os
  import yaml
  import glob
  import cv2
  import numpy
  import math
  import paddle
  import sys
  from paddle.inference import Config, create_predictor
  from python.utils import argsparser, Timer, get_current_memory_mb
  from python.benchmark_utils import PaddleInferBenchmark
  from python.infer import Detector, print_arguments
  from attr_infer import AttrDetector
label_desc: Use image node_id 2 for calling a global function with example usage: get_test_skeletons(input_file) and returns: list, output