code: def parameterized_truncated_normal(
      shape,
      means=0.0,
      stddevs=1.0,
      minvals=-2.0,
      maxvals=2.0,
      dtype=dtypes.float32,
      seed=None,
      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.
  
      Args:
        shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
        means: A 0-D Tensor or Python value of type `dtype`. The mean of the
          truncated normal distribution.
        stddevs: A 0-D Tensor or Python value of type `dtype`. The standard
          deviation of the truncated normal distribution.
        minvals: A 0-D Tensor or Python value of type `dtype`. The minimum value of
          the truncated normal distribution.
        maxvals: A 0-D Tensor or Python value of type `dtype`. The maximum value of
          the truncated normal distribution.
        dtype: The type of the output.
        seed: A Python integer. Used to create a random seed for the distribution.
          See
          `tf.random.set_seed`
          for behavior.
        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,
          "parameterized_truncated_normal",
          [shape, means, stddevs, minvals, maxvals],
      ) as name:
          shape_tensor = shape_util.shape_tensor(shape)
          means_tensor = ops.convert_to_tensor(
              means, dtype=dtype, name="means"
          )
          stddevs_tensor = ops.convert_to_tensor(
              stddevs, dtype=dtype, name="stddevs"
          )
          minvals_tensor = ops.convert_to_tensor(
              minvals, dtype=dtype, name="minvals"
          )
          maxvals_tensor = ops.convert_to_tensor(
              maxvals, dtype=dtype, name="maxvals"
          )
          seed1, seed2 = random_seed.get_seed(seed)
          rnd = gen_random_ops.parameterized_truncated_normal(
              shape_tensor,
              means_tensor,
              stddevs_tensor,
              minvals_tensor,
              maxvals_tensor,
              seed=seed1,
              seed2=seed2,
          )
          shape_util.maybe_set_static_shape(rnd, shape)
          return rnd
  
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 2 for calling a global function with example usage: parameterized_truncated_normal(shape, means, stddevs, minvals, maxvals, dtype, seed, name) and returns: rnd