class_docstr: Initializes by sampling from a normal distribution.
docstr: Constructs a :class:`RandomNormal` initializer.
  
  Args:
    stddev: The standard deviation of the normal distribution to sample from.
    mean: The mean of the normal distribution to sample from.
docstr_tok: ["Constructs","a",":","class",":","`","RandomNormal","`","initializer",".","Args",":","stddev",":","The","standard","deviation","of","the","normal","distribution","to","sample","from",".","mean",":","The","mean","of","the","normal","distribution","to","sample","from","."]
code: def __init__(self, stddev=1.0, mean=0.0):
      """Constructs a :class:`RandomNormal` initializer.
  
      Args:
        stddev: The standard deviation of the normal distribution to sample from.
        mean: The mean of the normal distribution to sample from.
      """
      self.stddev = stddev
      self.mean = mean
  
imports: from collections.abc import Sequence
  from typing import Any, Union
  from haiku._src import base
  from haiku._src.typing import Initializer
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 1 to create a new RandomNormal object from inherited base classes: hk.initializers with example: obj = RandomNormal(stddev, mean)