class_docstr: Random normal initializer.
  
  Draws samples from a normal distribution for given parameters.
  
  Examples:
  
  >>> # Standalone usage:
  >>> initializer = RandomNormal(mean=0.0, stddev=1.0)
  >>> values = initializer(shape=(2, 2))
  
  >>> # Usage in a Keras layer:
  >>> initializer = RandomNormal(mean=0.0, stddev=1.0)
  >>> layer = Dense(3, kernel_initializer=initializer)
  
  Args:
      mean: A python scalar or a scalar keras tensor. Mean of the random
          values to generate.
      stddev: A python scalar or a scalar keras tensor. Standard deviation of
         the random values to generate.
      seed: A Python integer or instance of
          `keras.backend.SeedGenerator`.
          Used to make the behavior of the initializer
          deterministic. Note that an initializer seeded with an integer
          or `None` (unseeded) will produce the same random values
          across multiple calls. To get different random values
          across multiple calls, use as seed an instance
          of `keras.backend.SeedGenerator`.
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code: def get_config(self):
      seed_config = serialization_lib.serialize_keras_object(
          self._init_seed
      )
      return {
          "mean": self.mean,
          "stddev": self.stddev,
          "seed": seed_config,
      }
  
imports: import math
  from keras import ops
  from keras.api_export import keras_export
  from keras.backend import random
  from keras.initializers.initializer import Initializer
  from keras.saving import serialization_lib
label_desc: Use image node_id 3 for calling the RandomNormal obj's underlying member method code with example usage: obj.get_config() and returns: dict