code: def stateless_random_normal(
      shape,
      seed,
      mean=0.0,
      stddev=1.0,
      dtype=dtypes.float32,
      name=None,
      alg="auto_select",
  ):
      """Outputs deterministic pseudorandom values from a normal distribution.
  
      This is a stateless version of `tf.random.normal`: if run twice with the
      same seeds and shapes, it will produce the same pseudorandom numbers.  The
      output is consistent across multiple runs on the same hardware (and between
      CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
      hardware.
  
      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.)
        mean: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
          distribution.
        stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
          of the normal distribution.
        dtype: The float type of the output: `float16`, `bfloat16`, `float32`,
          `float64`. Defaults to `float32`.
        name: A name for the operation (optional).
        alg: The RNG algorithm used to generate the random numbers. See
          `tf.random.stateless_uniform` for a detailed explanation.
  
      Returns:
        A tensor of the specified shape filled with random normal values.
      """
      with ops.name_scope(
          name, "stateless_random_normal", [shape, seed, mean, stddev]
      ) as name:
          shape = shape_util.shape_tensor(shape)
          mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
          stddev = ops.convert_to_tensor(
              stddev, dtype=dtype, name="stddev"
          )
          key, counter, alg = random_ops_util.get_key_counter_alg(
              seed, alg
          )
          rnd = gen_stateless_random_ops_v2.stateless_random_normal_v2(
              shape, key=key, counter=counter, dtype=dtype, alg=alg
          )
          result = math_ops.add(rnd * stddev, mean, name=name)
          shape_util.maybe_set_static_shape(result, shape)
          return result
  
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 9 for calling a global function with example usage: stateless_random_normal(shape, seed, mean, stddev, dtype, name, alg) and returns: result