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","]"]
code: def __init__(self, mean, std, tensor_shape=[3, 1, 1], inplace=False):
      if not isinstance(mean, Sequence):
          raise TypeError(
              "Mean must be list, tuple or np.ndarray, but got {type(mean)}"
          )
      if not isinstance(std, Sequence):
          raise TypeError(
              "Std must be list, tuple or np.ndarray, but got {type(std)}"
          )
  
      self.inplace = inplace
      if not inplace:
          self.mean = (
              np.array(mean).reshape(tensor_shape).astype(np.float32)
          )
          self.std = (
              np.array(std).reshape(tensor_shape).astype(np.float32)
          )
      else:
          self.mean = np.array(mean, dtype=np.float32)
          self.std = np.array(std, dtype=np.float32)
  
imports: import cv2
  import numpy
  from PIL import Image
  import paddle
label_desc: Use image node_id 1 to create a new Normalization object from inherited base classes: object with example: obj = Normalization(mean, std, tensor_shape, inplace)