class_docstr: Filter response normalization layer.
  
  Filter Response Normalization (FRN), a normalization
  method that enables models trained with per-channel
  normalization to achieve high accuracy. It performs better than
  all other normalization techniques for small batches and is par
  with Batch Normalization for bigger batch sizes.
  
  Arguments
      axis: List of axes that should be normalized. This should represent the
            spatial dimensions.
      epsilon: Small positive float value added to variance to avoid dividing by zero.
      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.
      learned_epsilon: (bool) Whether to add another learnable
      epsilon parameter or not.
      name: Optional name for the layer
  
  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. This layer, as of now,
      works on a 4-D tensor where the tensor should have the shape [N X H X W X C]
  
      TODO: Add support for NCHW data format and FC layers.
  
  Output shape
      Same shape as input.
  
  References
      - [Filter Response Normalization Layer: Eliminating Batch Dependence
      in the training of Deep Neural Networks]
      (https://arxiv.org/abs/1911.09737)
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code: def get_config(self):
      config = {
          "axis": self.axis,
          "epsilon": self.epsilon,
          "learned_epsilon": self.use_eps_learned,
          "beta_initializer": tf.keras.initializers.serialize(
              self.beta_initializer
          ),
          "gamma_initializer": tf.keras.initializers.serialize(
              self.gamma_initializer
          ),
          "beta_regularizer": tf.keras.regularizers.serialize(
              self.beta_regularizer
          ),
          "gamma_regularizer": tf.keras.regularizers.serialize(
              self.gamma_regularizer
          ),
          "beta_constraint": tf.keras.constraints.serialize(
              self.beta_constraint
          ),
          "gamma_constraint": tf.keras.constraints.serialize(
              self.gamma_constraint
          ),
          "learned_epsilon_constraint": tf.keras.constraints.serialize(
              self.eps_learned_constraint
          ),
      }
      base_config = super().get_config()
      return dict(**base_config, **config)
  
imports: import logging
  import tensorflow
  from typeguard import typechecked
  from tensorflow_addons.utils import types
label_desc: Use image node_id 4 for calling the FilterResponseNormalization obj's underlying member method code with example usage: obj.get_config() and returns: dict