text: import numpy as np
  from neural import LSTM_WindTurbine
  from neural import LSTM_PvSystem
  from neural import LSTM_LedSystem
  from neural import LSTM_LoadStationSystem
  from neural.example import LSTM
  
  
  # WIND TURBINE SYSTEM functions
  def predictPw(forecastHours, visualize):
      val = LSTM_WindTurbine.LSTM_RUN(
          "data/dummy/WindTurbine.csv", forecastHours, visualize
      )
      return val
  
  
  def getPw(forecastHours):
      val = LSTM_WindTurbine.LSTM_RUN("data/dummy/WindTurbine.csv")
      return val
  
  
  # PHOTOVOLTAIC SYSTEM functions
  def predictPpv(forecastHours, visualize):
      val = LSTM_PvSystem.LSTM_RUN(
          "data/dummy/PvSystem.csv", forecastHours, visualize
      )
      return val
  
  
  def getPpv(forecastHours):
      val = LSTM_PvSystem.LSTM_RUN("data/dummy/PvSystem.csv")
      return val
  
  
  # LED SYSTEM functions
  def predictPl(forecastHours, visualize):
      val = LSTM_LedSystem.LSTM_RUN(
          "data/dummy/LedSystem.csv", forecastHours, visualize
      )
      return val
  
  
  def getPl(forecastHours):
      val = LSTM_LedSystem.LSTM_RUN(
          "data/dummy/LedSystem.csv"
      )
      return val
  
  
  # POWER CONSUMPTION SYSTEM functions
  def predictPls(forecastHours, visualize):
      val = LSTM_LoadStationSystem.LSTM_RUN(
          "data/dummy/LoadStationSystem.csv", forecastHours, visualize
      )
      return val
  
  
  def getPls(forecastHours):
      val = LSTM_LoadStationSystem.LSTM_RUN("data/dummy/LoadStationSystem.csv")
      return val
  
  
  # Example and Dummy
  def example(forecastHours):
      return LSTM.LSTM_RUN("data/example/pollution.csv")
  
  
  def dummyResult(forecastHours):
      return (np.random.rand(1) * np.random.randint(1, 100))[0]
  
  
  def predictNeurals(forecastHours, visualize):
      Pw = predictPw(forecastHours, visualize)
      Ppv = predictPpv(forecastHours, visualize)
      Pl = predictPl(forecastHours, visualize)
      Pls = predictPls(forecastHours, visualize)
      return Pw, Ppv, Pl, Pls
  