code: def model(inpt, num_actions, scope, reuse=False):
      """This model takes as input an observation and returns values of all actions."""
      with tf.variable_scope(scope, reuse=reuse):
          out = inpt
          out = layers.fully_connected(
              out, num_outputs=64, activation_fn=tf.nn.tanh
          )
          out = layers.fully_connected(
              out, num_outputs=num_actions, activation_fn=None
          )
          return out
  
file_path: baselines/baselines/deepq/experiments/custom_cartpole.py
imports: import gym
  import itertools
  import numpy
  import tensorflow
  import tensorflow.contrib.layers
  import baselines.common.tf_util
  from baselines import logger
  from baselines import deepq
  from baselines.deepq.replay_buffer import ReplayBuffer
  from baselines.deepq.utils import ObservationInput
  from baselines.common.schedules import LinearSchedule
label_desc: Use image node_id 1 for calling a global function with example usage: model(inpt, num_actions, scope, reuse) and returns: out