code: def truncated_normal(
      shape,
      mean=0.0,
      stddev=1.0,
      dtype=dtypes.float32,
      seed=None,
      name=None,
  ):
      """Outputs random values from a truncated normal distribution.
  
      The values are drawn from a normal distribution with specified mean and
      standard deviation, discarding and re-drawing any samples that are more than
      two standard deviations from the mean.
  
      Examples:
  
      >>> tf.random.truncated_normal(shape=[2])
      <tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
  
      >>> tf.random.truncated_normal(shape=[2], mean=3, stddev=1, dtype=tf.float32)
      <tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
  
      Args:
        shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
        mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
          truncated normal distribution.
        stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
          of the normal distribution, before truncation.
        dtype: The type of the output. Restricted to floating-point types:
          `tf.half`, `tf.float`, `tf.double`, etc.
        seed: A Python integer. Used to create a random seed for the distribution.
          See `tf.random.set_seed` for more information.
        name: A name for the operation (optional).
  
      Returns:
        A tensor of the specified shape filled with random truncated normal values.
      """
      with ops.name_scope(
          name, "truncated_normal", [shape, mean, stddev]
      ) as name:
          shape_tensor = shape_util.shape_tensor(shape)
          mean_tensor = ops.convert_to_tensor(
              mean, dtype=dtype, name="mean"
          )
          stddev_tensor = ops.convert_to_tensor(
              stddev, dtype=dtype, name="stddev"
          )
          seed1, seed2 = random_seed.get_seed(seed)
          rnd = gen_random_ops.truncated_normal(
              shape_tensor, dtype, seed=seed1, seed2=seed2
          )
          mul = rnd * stddev_tensor
          value = math_ops.add(mul, mean_tensor, name=name)
          shape_util.maybe_set_static_shape(value, shape)
          return value
  
imports: import numpy
  from tensorflow.python.eager import context
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_random_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import shape_util
  from tensorflow.python.ops.gen_random_ops import *
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 3 for calling a global function with example usage: truncated_normal(shape, mean, stddev, dtype, seed, name) and returns: value