class_docstr: Performs standard ImageNet normalization.
  
  Note: ImageNet normalization is separated out as a module to fix bugs in the
  original DETR porting to JAX. Bugs:
    (1) 0.485 in mean_rgb was typoed as 0.48. (2) To be consistent with the
    pytorch implementation, rgb normalization should happen after data
    augmentation instead of before.
class_docstr_tok: ["Performs","standard","ImageNet","normalization",".","Note",":","ImageNet","normalization","is","separated","out","as","a","module","to","fix","bugs","in","the","original","DETR","porting","to","JAX",".","Bugs",":","(","1",")","0.485","in","mean_rgb","was","typoed","as","0.48",".","(","2",")","To","be","consistent","with","the","pytorch","implementation",",","rgb","normalization","should","happen","after","data","augmentation","instead","of","before","."]
code: def __init__(self):
      # Computed from a subset of the training set. The original (randomly
      # generated) subset used for calculation and the exact way of calculation is
      # not known to the public, but the numbers have been widely adopted since.
      self.mean_rgb = tf.constant(
          [0.485, 0.456, 0.406], dtype=tf.float32
      )
      self.std_rgb = tf.constant(
          [0.229, 0.224, 0.225], dtype=tf.float32
      )
  
imports: import functools
  from typing import Any, Callable, Mapping, Optional
  from absl import logging
  from flax import jax_utils
  import jax
  import jax.numpy
  from scenic.dataset_lib import dataset_utils
  from scenic.dataset_lib import datasets
  from scenic.dataset_lib.coco_dataset import coco_utils
  from scenic.projects.baselines.detr import transforms
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 1 to create a new ImageNetNormalization object  with example: obj = ImageNetNormalization()