code: def load_meanstd(self, cfg_transform):
      scale = 1.0
      mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
      std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
      for item in cfg_transform:
          if "NormalizeImage" in item:
              mean = np.array(
                  item["NormalizeImage"]["mean"], dtype=np.float32
              )
              std = np.array(
                  item["NormalizeImage"]["std"], dtype=np.float32
              )
              if item["NormalizeImage"].get("is_scale", True):
                  scale = 1.0 / 255.0
              break
      if self.data_format == "NHWC":
          self.scale = paddle.to_tensor(scale / std).reshape(
              (1, 1, 1, 3)
          )
          self.bias = paddle.to_tensor(-mean / std).reshape(
              (1, 1, 1, 3)
          )
      else:
          self.scale = paddle.to_tensor(scale / std).reshape(
              (1, 3, 1, 1)
          )
          self.bias = paddle.to_tensor(-mean / std).reshape(
              (1, 3, 1, 1)
          )
  
file_path: PaddleDetection/ppdet/modeling/architectures/meta_arch.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import paddle
  import paddle.nn
  import typing
  from ppdet.core.workspace import register
  from ppdet.modeling.post_process import nms
label_desc: Use image node_id 2 for calling the BaseArch obj's underlying member method code with example usage: obj.load_meanstd(cfg_transform) without return types