class_docstr: Instance normalization layer.
  
  Instance Normalization is an specific case of ```GroupNormalization```since
  it normalizes all features of one channel. The Groupsize is equal to the
  channel size. Empirically, its accuracy is more stable than batch norm in a
  wide range of small batch sizes, if learning rate is adjusted linearly
  with batch sizes.
  
  Arguments
      axis: Integer, the axis that should be normalized.
      epsilon: Small float added to variance to avoid dividing by zero.
      center: If True, add offset of `beta` to normalized tensor.
          If False, `beta` is ignored.
      scale: If True, multiply by `gamma`.
          If False, `gamma` is not used.
      beta_initializer: Initializer for the beta weight.
      gamma_initializer: Initializer for the gamma weight.
      beta_regularizer: Optional regularizer for the beta weight.
      gamma_regularizer: Optional regularizer for the gamma weight.
      beta_constraint: Optional constraint for the beta weight.
      gamma_constraint: Optional constraint for the gamma weight.
  
  Input shape
      Arbitrary. Use the keyword argument `input_shape`
      (tuple of integers, does not include the samples axis)
      when using this layer as the first layer in a model.
  
  Output shape
      Same shape as input.
  
  References
      - [Instance Normalization: The Missing Ingredient for Fast Stylization]
      (https://arxiv.org/abs/1607.08022)
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code: def __init__(self, **kwargs):
      if "groups" in kwargs:
          logging.warning(
              "The given value for groups will be overwritten."
          )
  
      kwargs["groups"] = -1
      super().__init__(**kwargs)
  
imports: import logging
  import tensorflow
  from typeguard import typechecked
  from tensorflow_addons.utils import types
label_desc: Use image node_id 1 to create a new InstanceNormalization object from inherited base classes: GroupNormalization with example: obj = InstanceNormalization()