code: def add_step_timing_signal(x, step, hparams):
      """Add n-dimensional embedding as the step (vertical) timing signal.
  
      Args:
        x: a tensor with shape [batch, length, depth]
        step: step
        hparams: model hyper parameters
  
      Returns:
        a Tensor with the same shape as x.
  
      """
      if hparams.recurrence_type == "act":
          num_steps = hparams.act_max_steps
      else:
          num_steps = hparams.num_rec_steps
      channels = common_layers.shape_list(x)[-1]
  
      if hparams.step_timing_signal_type == "learned":
          signal = common_attention.get_layer_timing_signal_learned_1d(
              channels, step, num_steps
          )
  
      elif hparams.step_timing_signal_type == "sinusoid":
          signal = common_attention.get_layer_timing_signal_sinusoid_1d(
              channels, step, num_steps
          )
  
      if hparams.add_or_concat_timing_signal == "add":
          x_with_timing = x + common_layers.cast_like(signal, x)
  
      elif hparams.add_or_concat_timing_signal == "concat":
          batch_size = common_layers.shape_list(x)[0]
          length = common_layers.shape_list(x)[1]
          signal_tiled = tf.tile(signal, [batch_size, length, 1])
          x_with_timing = tf.concat((x, signal_tiled), axis=-1)
  
      return x_with_timing
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import functools
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import expert_utils
  import tensorflow.compat.v1
label_desc: Use image node_id 21 for calling a global function with example usage: add_step_timing_signal(x, step, hparams) and returns: x_with_timing