code: def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
      
  
      return lambda *args, **kwargs: _cnn_to_mlp(convs, hiddens, dueling, layer_norm=layer_norm, *args, **kwargs)
docs: This model takes as input an observation and returns values of all actions.
  
      Parameters
      ----------
      convs: [(int, int, int)]
          list of convolutional layers in form of
          (num_outputs, kernel_size, stride)
      hiddens: [int]
          list of sizes of hidden layers
      dueling: bool
          if true double the output MLP to compute a baseline
          for action scores
      layer_norm: bool
          if true applies layer normalization for every layer
          as described in https://arxiv.org/abs/1607.06450
  
      Returns
      -------
      q_func: function
          q_function for DQN algorithm.