code: def create_arbitrary_trajectory():
      """Creates an arbitrary trajectory for unit testing BehavioralCloningAgent.
  
      This trajectory contains Tensors shaped `[6, 1, ...]` where `6` is the number
      of time steps and `1` is the batch.
  
      Observations are unbounded but actions are bounded to take values within
      `[1, 2]`. The action space is discrete.
  
      Policy info is not provided, as it is not used in BehavioralCloningAgent.
  
      Returns:
        traj: a hard coded `Trajectory` that matches time_step_spec and action_spec.
        time_step_spec: a hard coded time spec.
        action_spec: a hard coded action spec.
      """
  
      time_step_spec = ts.time_step_spec(
          tensor_spec.TensorSpec([], tf.int32, name="observation")
      )
      action_spec = tensor_spec.BoundedTensorSpec(
          [], tf.int32, minimum=1, maximum=2, name="action"
      )
  
      observation = tf.constant(
          [[1], [2], [3], [4], [5], [6]], dtype=tf.int32
      )
      action = tf.constant(
          [[1], [1], [1], [2], [2], [2]], dtype=tf.int32
      )
      reward = tf.constant(
          [[0], [0], [0], [0], [0], [0]], dtype=tf.float32
      )
      step_type = tf.constant(
          [[0], [1], [2], [0], [1], [2]], dtype=tf.int32
      )
      next_step_type = tf.constant(
          [[1], [2], [0], [1], [2], [0]], dtype=tf.int32
      )
      discount = tf.constant(
          [[1], [1], [1], [1], [1], [1]], dtype=tf.float32
      )
  
      traj = trajectory.Trajectory(
          observation=observation,
          action=action,
          policy_info=(),
          reward=reward,
          step_type=step_type,
          next_step_type=next_step_type,
          discount=discount,
      )
      return traj, time_step_spec, action_spec
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents import test_util
  from tf_agents.agents.behavioral_cloning import behavioral_cloning_agent
  from tf_agents.keras_layers import inner_reshape
  from tf_agents.networks import expand_dims_layer
  from tf_agents.networks import q_network
  from tf_agents.networks import q_rnn_network
  from tf_agents.networks import sequential
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 for calling a global function with example usage: create_arbitrary_trajectory() and returns: traj,  time_step_spec,  action_spec