class_docstr: Initialize parameter sampling from the normal distribution.
  
  Parameters
  ----------
  mean : int, float
      Mean of the normal distribution.
  
  std : int, float
      Standard deviation of the normal distribution.
  
  seed : None or int
      Random seed. Integer value will make results reproducible.
      Defaults to ``None``.
  
  Methods
  -------
  {Initializer.Methods}
class_docstr_tok: ["Initialize","parameter","sampling","from","the","normal","distribution",".","Parameters","--","--","--","--","--","mean",":","int",",","float","Mean","of","the","normal","distribution",".","std",":","int",",","float","Standard","deviation","of","the","normal","distribution",".","seed",":","None","or","int","Random","seed",".","Integer","value","will","make","results","reproducible",".","Defaults","to","``","None","``",".","Methods","--","--","--","-","{","Initializer.Methods","}"]
code: def sample(self, shape, return_array=False):
      if return_array:
          set_numpy_seed(self.seed)
          return np.random.normal(
              loc=self.mean, scale=self.std, size=shape
          )
  
      return tf.random_normal(
          mean=self.mean, stddev=self.std, shape=shape, seed=self.seed
      )
  
imports: import abc
  import six
  import numpy
  import tensorflow
  from neupy.core.docs import SharedDocsABCMeta
label_desc: Use image node_id 2 for calling the Normal obj's underlying member method code with example usage: obj.sample(shape, return_array) and returns: tf, np