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df = pd.read_csv('conllu.csv')
words = []
arcs = []
for index, row in df.iterrows():
    words.append({
        'text': row['word'],
        'tag': row['pos']
    })
    dep_head = row['dependency_head'] - 1
    dep_label = row['dependency_label']

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// Run in the console at https://trade.aliexpress.com/orderList.htm
Array.from(document.querySelectorAll('.amount-num')).map(x => parseFloat(x.innerText.replace('€ ','').replace('$ ',''))).reduce((a,b) => a+b)

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\begin{figure}[h]
    \centering

    \captionsetup{justification=centering}

    \begin{tikzpicture}
        [%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
            box/.style={rectangle,draw=black, thick, minimum size=0.5cm},
            dbox/.style={rectangle,draw=black, thick, minimum size=0.5cm, fill=black!20},
            gbox/.style={rectangle,draw=black, thick, minimum size=0.5cm, fill=black!5},

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\begin{figure}[h]
    \centering
    \captionsetup{justification=centering}
    \begin{tikzpicture}
        [%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
            box/.style={rectangle,draw=black, thick, minimum size=0.5cm},
            dbox/.style={rectangle,draw=black, thick, minimum size=0.5cm, fill=black!20},
        ]%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    % Bit flip

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\begin{figure}[h]
\centering
\captionsetup{justification=centering}
\begin{tikzpicture}[node distance = 4cm]
    \node [frame] (pop) {Population};
    \node [above=2cm, left of=pop] (init) {Initialisation};
    \node [below=2cm, left of=pop] (term) {Termination};
    \node [frame, above=2cm, right of=pop] (parents)  {Parents};
    \node [frame, below=2cm, right of=pop] (off)  {Offspring};
    

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\begin{tikzpicture}[node distance = 6em, auto, thick]
    \node [block] (Agent1) {Agent$_1$};
    \node [block, below of=Agent1] (Agent2) {Agent$_2$};
    \node [below of=Agent2] (Dots) {\cvdots};
    \node [block, below of=Dots] (Agent3) {Agent$_n$};
    \node [block, below of=Agent3] (Environment) {Environment};
    
     \path [line] (Agent1.0) --++ (10em,0em) |- node [near start]{$a_{1,t}$} (Environment.-15);
     \path [line] (Agent2.0) --++ (6em,0em) |- node [near start]{$a_{2,t}$} (Environment.0);
     \path [line] (Agent3.0) --++ (2em,0em) |- node [near start]{$a_{n,t}$} (Environment.15);

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def get_env():
    env = gym.make('FrostbiteDeterministic-v4')
    env = NoopResetEnv(env, noop_max=30)
    env = WarpFrame(env, 84)
    env = FrameStack(env, 4)
    return env

register_env("frostbite", get_env)

config = {

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class GATrainer(Trainer):
    _name = "GA"

    @override(Trainer)
    def _init(self, config, env_creator):
        self.config = config
        self._workers = [
            Worker.remote(config, env_creator)
            for _ in range(config["num_workers"])
        ]

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@ray.remote
class Worker:
    def __init__(self,
                 config,
                 env_creator):
        self.maximum_timesteps = config['max_timesteps_per_episode']
        self.mutation_power = config['mutation_power']
        self.model = AtariModel()
        self.env = env_creator({})

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class AtariModel:
    def __init__(self):
        inputs = Input(shape=(84, 84, 4))
        layer1 = Conv2D(32, [8, 8], strides=(4, 4), activation="relu")(inputs)
        layer2 = Conv2D(64, [4, 4], strides=(2, 2), activation="relu")(layer1)
        layer3 = Conv2D(64, [3, 3], strides=(1, 1), activation="relu")(layer2)
        layer4 = Flatten()(layer3)
        layer5 = Dense(512, activation="relu")(layer4)
        action = Dense(6)(layer5)
        self.model = Model(inputs=inputs, outputs=action)