class_docstr: Initializer that generates a truncated normal distribution.
  
  These values are similar to values from a `RandomNormal`
  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.
  
  
  Parameters
  ----------
  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 andom values to generate.
  seed : A Python integer.
      Used to seed the random generator.
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code: def __call__(self, shape, dtype=tf.float32):
      return tf.random.truncated_normal(
          shape, self.mean, self.stddev, dtype=dtype, seed=self.seed
      )
  
imports: import numpy
  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