class_docstr: Fast implementation of normalization and type conversion for uint8 images
  to any floating point dtype.
  
  Works on both GPU and CPU tensors.
  
  Parameters
  ----------
  mean: np.ndarray
      The mean vector.
  std: np.ndarray
      The standard deviation vector.
  type: np.dtype
      The desired output type for the result as a numpy type.
      If the transform is applied on a GPU tensor it will be converted
      as the equivalent torch dtype.
class_docstr_tok: ["Fast","implementation","of","normalization","and","type","conversion","for","uint8","images","to","any","floating","point","dtype",".","Works","on","both","GPU","and","CPU","tensors",".","Parameters","--","--","--","--","--","mean",":","np.ndarray","The","mean","vector",".","std",":","np.ndarray","The","standard","deviation","vector",".","type",":","np.dtype","The","desired","output","type","for","the","result","as","a","numpy","type",".","If","the","transform","is","applied","on","a","GPU","tensor","it","will","be","converted","as","the","equivalent","torch","dtype","."]
code: def generate_code(self) -> Callable:
      if self.mode == "cpu":
          return self.generate_code_cpu()
      return self.generate_code_gpu()
  
file_path: ffcv/ffcv/transforms/normalize.py
imports: from collections.abc import Sequence
  from typing import Tuple
  import numpy
  import torch
  from numpy import dtype
  from numpy.random import rand
  from dataclasses import replace
  from typing import Callable, Optional, Tuple
  from ..pipeline.allocation_query import AllocationQuery
  from ..pipeline.operation import Operation
  from ..pipeline.state import State
  from ..pipeline.compiler import Compiler
label_desc: Use image node_id 2 for calling the NormalizeImage obj's underlying member method code with example usage: obj.generate_code() and returns: self, self