class_docstr: Perform feature-wise normalization on data.
  
  Refer to Normalization layer in keras preprocessing layers for more
  information.
  
  # Arguments
      axis: Integer or tuple of integers, the axis or axes that should be
          normalized (typically the features axis). We will normalize each
          element in the specified axis. The default is '-1' (the innermost
          axis); 0 (the batch axis) is not allowed.
class_docstr_tok: ["Perform","feature-wise","normalization","on","data",".","Refer","to","Normalization","layer","in","keras","preprocessing","layers","for","more","information",".","#","Arguments","axis",":","Integer","or","tuple","of","integers",",","the","axis","or","axes","that","should","be","normalized","(","typically","the","features","axis",")",".","We","will","normalize","each","element","in","the","specified","axis",".","The","default","is","'-1","'","(","the","innermost","axis",")",";","0","(","the","batch","axis",")","is","not","allowed","."]
code: def build(self, hp, inputs=None):
      input_node = nest.flatten(inputs)[0]
      return layers.Normalization(axis=self.axis)(input_node)
  
imports: from typing import Optional
  from typing import Tuple
  from typing import Union
  from keras_tuner.engine import hyperparameters
  from tensorflow import nest
  from tensorflow.keras import layers
  from autokeras import analysers
  from autokeras import keras_layers
  from autokeras.engine import block
  from autokeras.utils import io_utils
  from autokeras.utils import utils
label_desc: Use image node_id 2 for calling the Normalization obj's underlying member method code with example usage: obj.build(hp, inputs) and returns: layers