input: Write a function to predict stock market movements using a deep learning model. The function should split the input data into training and testing sets, build a deep learning model using TensorFlow and Keras, compile, train, and evaluate the model. Return the evaluation score of the model.
  
  def predict_stock_movements(data):
      #import libraries
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      #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
      #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)
      return score
  
  assert predict_stock_movements(pd.DataFrame({'X': [1, 2, 3, 4], 'y': [5, 6, 7, 8]}))[0] >= 0
  assert predict_stock_movements(pd.DataFrame({'X': [10, 20, 30, 40], 'y': [5, 6, 7, 8]}))[0] >= 0
  assert predict_stock_movements(pd.DataFrame({'X': [1, 2, 3, 4], 'y': [50, 60, 70, 80]}))[0] >= 0
  assert predict_stock_movements(pd.DataFrame({'X': [10, 20, 30, 40], 'y': [50, 60, 70, 80]}))[0] >= 0
  assert predict_stock_movements(pd.DataFrame({'X': [], 'y': []})) is None
output: def predict_stock_movements(data):
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      if data.empty:
          return None
  
      X_train = data.iloc[:, 0].values.reshape(-1, 1)
      y_train = data.iloc[:, 1].values
      X_test = data.iloc[:, 0].values.reshape(-1, 1)
      y_test = data.iloc[:, 1].values
  
      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))
  
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      score = model.evaluate(X_test, y_test, verbose=0)
      return score