class_docstr: The :class:`GroupNorm` layer is for Group Normalization.
  See `tf.contrib.layers.group_norm <https://www.tensorflow.org/api_docs/python/tf/contrib/layers/group_norm>`__.
  
  Parameters
  -----------
  # prev_layer : :class:`Layer`
  #     The previous layer.
  groups : int
      The number of groups
  act : activation function
      The activation function of this layer.
  epsilon : float
      Eplison.
  data_format : str
      channels_last 'channel_last' (default) or channels_first.
  name : None or str
      A unique layer name
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code: def __init__(
      self,
      groups=32,
      epsilon=1e-06,
      act=None,
      data_format="channels_last",
      name=None,
  ):  #'groupnorm'):
      # super(GroupNorm, self).__init__(prev_layer=prev_layer, act=act, name=name)
      super().__init__(name, act=act)
      self.groups = groups
      self.epsilon = epsilon
      self.data_format = data_format
  
      logging.info(
          "GroupNorm %s: act: %s"
          % (
              self.name,
              self.act.__name__
              if self.act is not None
              else "No Activation",
          )
      )
  
imports: import tensorflow
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.training import moving_averages
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.layers.core import Layer
label_desc: Use image node_id 1 to create a new GroupNorm object from inherited base classes: Layer with example: obj = GroupNorm(groups, epsilon, act, data_format, name)