class_docstr: Policy object that implements a DQN policy, using a feed forward neural network.
  
  :param sess: (TensorFlow session) The current TensorFlow session
  :param ob_space: (Gym Space) The observation space of the environment
  :param ac_space: (Gym Space) The action space of the environment
  :param n_env: (int) The number of environments to run
  :param n_steps: (int) The number of steps to run for each environment
  :param n_batch: (int) The number of batch to run (n_envs * n_steps)
  :param reuse: (bool) If the policy is reusable or not
  :param layers: ([int]) The size of the Neural network for the policy (if None, default to [64, 64])
  :param cnn_extractor: (function (TensorFlow Tensor, ``**kwargs``): (TensorFlow Tensor)) the CNN feature extraction
  :param feature_extraction: (str) The feature extraction type ("cnn" or "mlp")
  :param obs_phs: (TensorFlow Tensor, TensorFlow Tensor) a tuple containing an override for observation placeholder
      and the processed observation placeholder respectively
  :param layer_norm: (bool) enable layer normalisation
  :param dueling: (bool) if true double the output MLP to compute a baseline for action scores
  :param act_fun: (tf.func) the activation function to use in the neural network.
  :param kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
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code: def proba_step(self, obs, state=None, mask=None):
      return self.sess.run(self.policy_proba, {self.obs_ph: obs})
  
imports: import tensorflow
  import tensorflow.contrib.layers
  import numpy
  from gym.spaces import Discrete
  from stable_baselines.common.policies import BasePolicy, nature_cnn, register_policy
label_desc: Use image node_id 3 for calling the FeedForwardPolicy obj's underlying member method code with example usage: obj.proba_step(obs, state, mask) and returns: self