input: Write a function that generates a Python script for predicting stock market movements using a deep learning model. The script should include the necessary imports, data reading, splitting into training and testing sets, building and compiling the model, training the model, and evaluating the model. Return the generated script as the output.  
  def generate_stock_prediction_script():  
      script = \  
      "#import libraries\n" \  
      "import pandas as pd\n" \  
      "import numpy as np\n" \  
      "import matplotlib.pyplot as plt\n" \  
      "import seaborn as sns\n" \  
      "\n" \  
      "#import data\n" \  
      "data = pd.read_csv('data.csv') \n" \  
      "\n" \  
      "#split data into train and test data\n" \  
      "X_train=data.iloc[:, 0] #Choosing the independent variable \n" \  
      "y_train=data.iloc[:, 1]  #Choosing the dependent variable\n" \  
      "X_test=data.iloc[:, 0] #Choosing the independent variable \n" \  
      "y_test=data.iloc[:, 1]  #Choosing the dependent variable\n" \  
      "\n" \  
      "#Build a deep learning model\n" \  
      "#import necessary libraries\n" \  
      "import tensorflow as tf\n" \  
      "from tensorflow.keras.layers import Dense\n" \  
      "from tensorflow.keras.models import Sequential\n" \  
      "\n" \  
      "#create a model and add layers\n" \  
      "model = Sequential()\n" \  
      "model.add(Dense(64, input_shape=(1,), activation='relu'))\n" \  
      "model.add(Dense(128, activation='relu'))\n" \  
      "model.add(Dense(64, activation='relu'))\n" \  
      "model.add(Dense(1))\n" \  
      "\n" \  
      "#compile the model\n" \  
      "model.compile(optimizer='adam', loss='mse', metrics=['mse'])\n" \  
      "\n" \  
      "#train the model\n" \  
      "model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))\n" \  
      "\n" \  
      "#Evaluate the model\n" \  
      "score = model.evaluate(X_test, y_test, verbose=0)\n" \  
      "\n" \  
      "return script"  
  assert generate_stock_prediction_script() == """  
  #import libraries  
  import pandas as pd  
  import numpy as np  
  import matplotlib.pyplot as plt  
  import seaborn as sns  
    
  #import data  
  data = pd.read_csv('data.csv')  
    
  #split data into train and test data  
  X_train=data.iloc[:, 0] #Choosing the independent variable  
  y_train=data.iloc[:, 1]  #Choosing the dependent variable  
  X_test=data.iloc[:, 0] #Choosing the independent variable  
  y_test=data.iloc[:, 1]  #Choosing the dependent variable  
    
  #Build a deep learning model  
  #import necessary libraries  
  import tensorflow as tf  
  from tensorflow.keras.layers import Dense  
  from tensorflow.keras.models import Sequential  
    
  #create a model and add layers  
  model = Sequential()  
  model.add(Dense(64, input_shape=(1,), activation='relu'))  
  model.add(Dense(128, activation='relu'))  
  model.add(Dense(64, activation='relu'))  
  model.add(Dense(1))  
    
  #compile the model  
  model.compile(optimizer='adam', loss='mse', metrics=['mse'])  
    
  #train the model  
  model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))  
    
  #Evaluate the model  
  score = model.evaluate(X_test, y_test, verbose=0)  
  """  
  assert generate_stock_prediction_script() == True
output: def generate_stock_prediction_script():
      script = \
      "#import libraries\n" \
      "import pandas as pd\n" \
      "import numpy as np\n" \
      "import matplotlib.pyplot as plt\n" \
      "import seaborn as sns\n" \
      "\n" \
      "#import data\n" \
      "data = pd.read_csv('data.csv') \n" \
      "\n" \
      "#split data into train and test data\n" \
      "X_train=data.iloc[:, 0] #Choosing the independent variable \n" \
      "y_train=data.iloc[:, 1]  #Choosing the dependent variable\n" \
      "X_test=data.iloc[:, 0] #Choosing the independent variable \n" \
      "y_test=data.iloc[:, 1]  #Choosing the dependent variable\n" \
      "\n" \
      "#Build a deep learning model\n" \
      "#import necessary libraries\n" \
      "import tensorflow as tf\n" \
      "from tensorflow.keras.layers import Dense\n" \
      "from tensorflow.keras.models import Sequential\n" \
      "\n" \
      "#create a model and add layers\n" \
      "model = Sequential()\n" \
      "model.add(Dense(64, input_shape=(1,), activation='relu'))\n" \
      "model.add(Dense(128, activation='relu'))\n" \
      "model.add(Dense(64, activation='relu'))\n" \
      "model.add(Dense(1))\n" \
      "\n" \
      "#compile the model\n" \
      "model.compile(optimizer='adam', loss='mse', metrics=['mse'])\n" \
      "\n" \
      "#train the model\n" \
      "model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))\n" \
      "\n" \
      "#Evaluate the model\n" \
      "score = model.evaluate(X_test, y_test, verbose=0)\n" \
      "\n" \
      "return script"