code: def _get_empirical_parameters():
      """Gets the number of parameters built into the current Tensorflow graph."""
      trainable_variables_list = tf.trainable_variables()
  
      empirical_num_params = 0
      for variable_tensor in trainable_variables_list:
          empirical_num_params += np.prod(variable_tensor.shape)
  
      return empirical_num_params
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import itertools
  from absl.testing import parameterized
  import numpy
  from tensor2tensor.layers import common_attention
  from tensor2tensor.models import transformer
  from tensor2tensor.models.neural_architecture_search import nas_layers
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: _get_empirical_parameters() and returns: empirical_num_params