class_docstr: Normalization.
  Args:
      mean(Sequence[float]): mean values of different channels.
      std(Sequence[float]): std values of different channels.
      tensor_shape(list): size of mean, default [3,1,1]. For slowfast, [1,1,1,3]
class_docstr_tok: ["Normalization",".","Args",":","mean","(","Sequence","[","float","]",")",":","mean","values","of","different","channels",".","std","(","Sequence","[","float","]",")",":","std","values","of","different","channels",".","tensor_shape","(","list",")",":","size","of","mean",",","default","[","3,1,1","]",".","For","slowfast",",","[","1,1,1,3","]"]
docstr: Performs normalization operations.
  Args:
      imgs: Numpy array.
  return:
      np_imgs: Numpy array after normalization.
docstr_tok: ["Performs","normalization","operations",".","Args",":","imgs",":","Numpy","array",".","return",":","np_imgs",":","Numpy","array","after","normalization","."]
code: def __call__(self, results):
      """
      Performs normalization operations.
      Args:
          imgs: Numpy array.
      return:
          np_imgs: Numpy array after normalization.
      """
  
      if self.inplace:  # default is False
          n = len(results["imgs"])
          h, w, c = results["imgs"][0].shape
          norm_imgs = np.empty((n, h, w, c), dtype=np.float32)
          for i, img in enumerate(results["imgs"]):
              norm_imgs[i] = img
  
          for img in norm_imgs:  # [n,h,w,c]
              mean = np.float64(self.mean.reshape(1, -1))  # [1, 3]
              stdinv = 1 / np.float64(self.std.reshape(1, -1))  # [1, 3]
              cv2.subtract(img, mean, img)
              cv2.multiply(img, stdinv, img)
      else:
          imgs = results["imgs"]
          norm_imgs = imgs / 255.0
          norm_imgs -= self.mean
          norm_imgs /= self.std
          if "backend" in results and results["backend"] == "pyav":
              norm_imgs = paddle.to_tensor(
                  norm_imgs, dtype=paddle.float32
              )
      results["imgs"] = norm_imgs
      return results
  
imports: import cv2
  import numpy
  from PIL import Image
  import paddle
label_desc: Use image node_id 2 for calling the Normalization obj's underlying member method code with example usage: obj.__call__(results) and returns: results