class_docstr: Initialize the weight by drawing sample from truncated normal distribution with
  provided mean and standard deviation. Values whose magnitude is more than 2 standard deviations
  from the mean are dropped and re-picked.
  
  In the implementation, we used the method described in
  https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf, which is also
  obtained in PyTorch.
  
  Parameters
  ----------
  mean
      Mean of the underlying normal distribution
  stdev
      Standard deviation of the underlying normal distribution
  scale
      The scale of the truncated distribution.
      The values
  **kwargs
      Additional parameters for base Initializer.
code: def __init__(
      self, mean: float = 0, stdev: float = 0.01, scale=2, **kwargs
  ):
      super().__init__(**kwargs)
      self._mean = mean
      self._stdev = stdev
      self._scale = scale
      self._a = mean - scale * stdev
      self._b = mean + scale * stdev
      if (mean < self._a - 2 * stdev) or (mean > self._b + 2 * stdev):
          warnings.warn(
              "mean is more than 2 std from [a, b] in init.TruncNorm. "
              "The distribution of values may be incorrect.",
              stacklevel=2,
          )
      self._l = norm_cdf(-scale)
      self._u = norm_cdf(scale)
  
file_path: gluon-nlp/src/gluonnlp/initializer.py
imports: import warnings
  import math
  import mxnet
  from mxnet.initializer import Initializer
label_desc: Use image node_id 1 to create a new TruncNorm object from inherited base classes: Initializer with example: obj = TruncNorm(mean, stdev, scale)