code: def truncated_normal(
      shape=None, mean=0.0, stddev=0.02, dtype=tf.float32, seed=None
  ):
      """Truncated Normal.
  
      Initialization with random values from a normal truncated distribution.
  
      The generated values follow a normal distribution with specified mean and
      standard deviation, except that values whose magnitude is more than 2 standard
      deviations from the mean are dropped and re-picked.
  
      Arguments:
          shape: List of `int`. A shape to initialize a Tensor (optional).
          mean: Same as `dtype`. The mean of the truncated normal distribution.
          stddev: Same as `dtype`. The standard deviation of the truncated
              normal distribution.
          dtype: The tensor data type.
          seed: `int`. Used to create a random seed for the distribution.
  
      Returns:
          The Initializer, or an initialized `Tensor` if shape is specified.
  
      """
      if shape:
          return tf.truncated_normal(
              shape=shape,
              mean=mean,
              stddev=stddev,
              seed=seed,
              dtype=dtype,
          )
      else:
          return tf.truncated_normal_initializer(
              mean=mean, stddev=stddev, seed=seed, dtype=dtype
          )
  
file_path: tflearn/tflearn/initializations.py
imports: from __future__ import division, print_function, absolute_import
  import math
  import tensorflow.compat.v1
  from .utils import get_from_module
label_desc: Use image node_id 6 for calling a global function with example usage: truncated_normal(shape, mean, stddev, dtype, seed) and returns: tf, tf