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def build_neural_net(self):
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(128, activation='relu', input_shape=(input_shape,)))
    model.add(tf.keras.layers.Dense(64, activation='relu'))
    model.add(tf.keras.layers.Dense(output_shape, activation='softmax'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

def think_smart_idea(self):
    market_prediction = self.market_predictor.predict_market()
    if market_prediction == "favorable":
        return self.neural_net.predict(np.random.random(input_shape))
    else:
        return "wait_for_better_conditions"

def is_legal(self, idea):
    return check_legality(idea)

def find_legal_loopholes(self):
    legal_loopholes = search_global_constructions_for_loopholes()
    self.knowledge_base.update_legal_knowledge(legal_loopholes)

def invest_in_stocks(self):
    best_stocks = analyze_market_trends()
    if best_stocks:
        chosen_stock = self.knowledge_base.select_best_stock(best_stocks)
        if chosen_stock.potential > 0.5:
            investment_amount = calculate_optimal_investment(chosen_stock)
            possible_returns = calculate_possible_returns(chosen_stock, investment_amount)
            best_continuations = select_best_continuations(possible_returns, 10)
            for continuation in best_continuations:
                execute_action(continuation)

def execute_idea(self, idea):
    if idea == "wait_for_better_conditions":
        return
    else:
        execute_action(idea)

def learn_from_errors(self):
    for idea, error in self.error_memory.items():
        if error == "legal_loophole":
            self.knowledge_base.update_legal_knowledge(idea)
        else:
            self.knowledge_base.improve_execution(idea, error, self.learning_rate)

def make_decision(self, context):
    if context == "invest":
        self.invest_in_stocks()
    else:
        genius_idea = self.think_smart_idea()
        self.execute_idea(genius_idea)

def continue_improvement(self):
    for _ in range(5000):
        context = self.knowledge_base.get_decision_context()
        self.make_decision(context)
        self.learn_from_errors()

def run_toast_plus(self):
    while not self.knowledge_base.is_AI_highly_complex_and_functional():
        self.continue_improvement()

def build_neural_net(self):
    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Dense(128, activation='relu', input_shape=(market_input_shape,)))
    model.add(tf.keras.layers.Dense(64, activation='relu'))
    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model

def predict_market(self):
    return "favorable" if self.neural_net.predict(np.random.random(market_input_shape)) > 0.5 else "unfavorable"