code: def create_dqn_agent_and_dataset_fn(
      action_spec, time_step_spec, train_step, batch_size
  ):
      """Builds and returns a dataset function for DQN Agent."""
      q_net = build_dummy_sequential_net(
          fc_layer_params=(100,), action_spec=action_spec
      )
  
      agent = dqn_agent.DqnAgent(
          time_step_spec,
          action_spec,
          q_network=q_net,
          optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
          train_step_counter=train_step,
      )
      agent.initialize()
  
      def make_item(_):
          traj = tensor_spec.sample_spec_nest(
              agent.collect_data_spec, seed=123, outer_dims=[2]
          )
  
          def scale_observation_only(item):
              # Scale float values in the sampled item by large value to avoid NaNs.
              if item.dtype == tf.float32:
                  return tf.math.divide(item, 1.0e22)
              else:
                  return item
  
          return tf.nest.map_structure(scale_observation_only, traj)
  
      l = []
      for i in range(100):
          l.append([i])
      dataset = tf.data.Dataset.zip(
          (
              tf.data.Dataset.from_tensor_slices(l).map(make_item),
              tf.data.experimental.Counter(),
          )
      )
      dataset_fn = lambda: dataset.batch(batch_size)
  
      return agent, dataset, dataset_fn, agent.collect_data_spec
  
imports: import functools
  from absl import logging
  import numpy
  import reverb
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.agents.ppo import ppo_clip_agent
  from tf_agents.environments import suite_gym
  from tf_agents.experimental.distributed import reverb_variable_container
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import sequential
  from tf_agents.networks import value_network
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.specs import tensor_spec
  from tf_agents.train import actor
  from tf_agents.train.utils import replay_buffer_utils
  from tf_agents.train.utils import spec_utils
  from tf_agents.train.utils import train_utils
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
label_desc: Use image node_id 5 for calling a global function with example usage: create_dqn_agent_and_dataset_fn(action_spec, time_step_spec, train_step, batch_size) and returns: agent,  dataset,  dataset_fn,  agent, tf, tf, item