input: 
  def train_stock_market_prediction_model():
      """ This function creates and trains a deep learning model to predict stock market movements.
      It first imports relevant libraries, loads a dataset from a CSV file, splits the data into training 
      and testing sets, builds a neural network model, compiles it with an optimizer and loss function, trains the model, 
      and evaluates it with the test data.
      >>> train_stock_market_prediction_model()
      32.12
      """
  
output:     
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load dataset
      data = pd.read_csv('stock_market_data.csv')
  
      # Split data into features and target
      X = data.drop('target', axis=1)
      y = data['target']
  
      # Split data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Build the neural network model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Evaluate the model
      loss = model.evaluate(X_test, y_test, verbose=0)
      return loss
  