code: def get_number_of_trainable_elements(
      network: types.Network,
  ) -> types.Float:
      """Gets the total # of elements in the network's trainable variables.
  
      Args:
        network: A `types.Network`.
  
      Returns:
        The total number of elements in the network's trainable variables.
      """
      num_elements_list = []
      for var in network.trainable_variables:
          num_elements = var.get_shape().num_elements()
          if num_elements is None:
              raise ValueError(
                  f"Variable:{var} is expected to have a known shape, but found "
                  "otherwise."
              )
          num_elements_list.append(num_elements)
      return sum(num_elements_list)
  
file_path: agents/tf_agents/bandits/policies/falcon_reward_prediction_policy.py
imports: from typing import Iterable, Optional, Sequence, Text, Tuple
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.policies import reward_prediction_base_policy
  from tf_agents.distributions import shifted_categorical
  from tf_agents.policies import utils
  from tf_agents.typing import types
label_desc: Use image node_id 1 for calling a global function with example usage: get_number_of_trainable_elements(network) and returns: sum