class_docstr: Initializer that generates a truncated normal distribution.
  
  These values follow a normal distribution except that values more than two
  standard deviations from the mean are discarded and re-drawn. This is the
  recommended initializer for neural network weights and filters.
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docstr: Constructs a truncated normal initializer.
  
  Args:
    mean: A python scalar or a scalar tensor. Mean of the random values to
      generate.
    stddev: A python scalar or a scalar tensor. Standard deviation of the
      random values to generate.
    seed: The seed used in the generation of random numbers.
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code: def __init__(
      self,
      mean: types.FloatLike = 0.0,
      stddev: types.FloatLike = 1.0,
      seed: Optional[int] = None,
  ):
      """Constructs a truncated normal initializer.
  
      Args:
        mean: A python scalar or a scalar tensor. Mean of the random values to
          generate.
        stddev: A python scalar or a scalar tensor. Standard deviation of the
          random values to generate.
        seed: The seed used in the generation of random numbers.
      """
      self.mean = mean
      self.stddev = stddev
      self.seed = seed
  
imports: import abc
  import collections
  from typing import Iterable, Mapping, Optional, Union
  import numpy
  from sonnet.src import types
  import tensorflow
label_desc: Use image node_id 1 to create a new TruncatedNormal object from inherited base classes: Initializer with example: obj = TruncatedNormal(mean, stddev, seed)