code: def _concat_and_flatten(traj, multiplier):
      """Concatenate tensors in the input trajectory by `multiplier` times.
  
      Args:
        traj: a `Trajectory` shaped [batch_size, num_steps, ...].
        multiplier: the number of times to concatenate the input trajectory.
  
      Returns:
        a flattened `Trajectory` shaped [multiplier * batch_size * num_steps, ...].
      """
  
      def concat_and_flatten_tensor(tensor):
          multiplied_component_list = [tensor] * multiplier
          concat_tensor = tf.concat(multiplied_component_list, axis=0)
  
          first_dim = multiplier * tensor.shape[0] * tensor.shape[1]
          other_dims = tensor.shape[2:]
          return tf.reshape(
              concat_tensor, shape=(first_dim,) + other_dims
          )
  
      concated_traj = tf.nest.map_structure(
          concat_and_flatten_tensor, traj
      )
      return concated_traj
  
imports: from absl import flags
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.agents import tf_agent
  from tf_agents.agents.ppo import ppo_actor_network
  from tf_agents.agents.ppo import ppo_agent
  from tf_agents.networks import value_network
  from tf_agents.specs import tensor_spec
  from tf_agents.train import ppo_learner
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling a global function with example usage: _concat_and_flatten(traj, multiplier) and returns: concated_traj, tf