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.
class_docstr_tok: ["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","."]
code: def __call__(self, shape: types.ShapeLike, dtype: tf.DType):
      dtype = _as_floating_dtype(dtype)
      return tf.random.truncated_normal(
          shape=shape,
          mean=self.mean,
          stddev=self.stddev,
          dtype=dtype,
          seed=self.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 2 for calling the TruncatedNormal obj's underlying member method code with example usage: obj.__call__(shape, dtype) and returns: tf