input: 
  def predict_stock_movement(data: pd.DataFrame) -> float:
      """ Predict the stock market movement using a deep learning model built in TensorFlow.
      The function takes a DataFrame with a single feature as input and contains the stock prices and 
      corresponding target values. The model is trained to predict future stock prices based on past data.
      >>> data = pd.read_csv('data.csv')
      >>> score = predict_stock_movement(data)
      >>> print(score)
      224.785
      """
      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
      
      model = tf.keras.models.Sequential()
      model.add(tf.keras.layers.Dense(64, input_shape=(1,), activation='relu'))
      model.add(tf.keras.layers.Dense(128, activation='relu'))
      model.add(tf.keras.layers.Dense(64, activation='relu'))
      model.add(tf.keras.layers.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[0]
  
output:     import tensorflow as tf
      import pandas as pd