code: def stateless_parameterized_truncated_normal(
      shape,
      seed,
      means=0.0,
      stddevs=1.0,
      minvals=-2.0,
      maxvals=2.0,
      name=None,
  ):
      """Outputs random values from a truncated normal 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.
  
  
      Examples:
  
      Sample from a Truncated normal, with deferring shape parameters that
      broadcast.
  
      >>> means = 0.
      >>> stddevs = tf.math.exp(tf.random.uniform(shape=[2, 3]))
      >>> minvals = [-1., -2., -1000.]
      >>> maxvals = [[10000.], [1.]]
      >>> y = tf.random.stateless_parameterized_truncated_normal(
      ...   shape=[10, 2, 3], seed=[7, 17],
      ...   means=means, stddevs=stddevs, minvals=minvals, maxvals=maxvals)
      >>> y.shape
      TensorShape([10, 2, 3])
  
      Args:
        shape: A 1-D integer `Tensor` or Python array. The shape of the output
          tensor.
        seed: A shape [2] Tensor, the seed to the random number generator. Must have
          dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
        means: A `Tensor` or Python value of type `dtype`. The mean of the truncated
          normal distribution. This must broadcast with `stddevs`, `minvals` and
          `maxvals`, and the broadcasted shape must be dominated by `shape`.
        stddevs: A `Tensor` or Python value of type `dtype`. The standard deviation
          of the truncated normal distribution. This must broadcast with `means`,
          `minvals` and `maxvals`, and the broadcasted shape must be dominated by
          `shape`.
        minvals: A `Tensor` or Python value of type `dtype`. The minimum value of
          the truncated normal distribution. This must broadcast with `means`,
          `stddevs` and `maxvals`, and the broadcasted shape must be dominated by
          `shape`.
        maxvals: A `Tensor` or Python value of type `dtype`. The maximum value of
          the truncated normal distribution. This must broadcast with `means`,
          `stddevs` and `minvals`, and the broadcasted shape must be dominated by
          `shape`.
        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,
          "stateless_parameterized_truncated_normal",
          [shape, means, stddevs, minvals, maxvals],
      ) as name:
          shape_tensor = shape_util.shape_tensor(shape)
          means_tensor = ops.convert_to_tensor(means, name="means")
          stddevs_tensor = ops.convert_to_tensor(
              stddevs, name="stddevs"
          )
          minvals_tensor = ops.convert_to_tensor(
              minvals, name="minvals"
          )
          maxvals_tensor = ops.convert_to_tensor(
              maxvals, name="maxvals"
          )
          rnd = gen_stateless_random_ops.stateless_parameterized_truncated_normal(
              shape_tensor,
              seed,
              means_tensor,
              stddevs_tensor,
              minvals_tensor,
              maxvals_tensor,
          )
          shape_util.maybe_set_static_shape(rnd, shape)
          return rnd
  
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import gen_random_index_shuffle_ops
  from tensorflow.python.ops import gen_stateless_random_ops
  from tensorflow.python.ops import gen_stateless_random_ops_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.ops import shape_util
  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 14 for calling a global function with example usage: stateless_parameterized_truncated_normal(shape, seed, means, stddevs, minvals, maxvals, name) and returns: rnd