input: def predict_stock_market_movement() -> float:
      """ 
      This function trains a deep learning model using TensorFlow to predict stock market movements based on historical data in 'data.csv'. 
      The model is built with multiple dense layers and compiled with the Adam optimizer and mean squared error loss function. 
      It is trained for 20 epochs with a batch size of 64, and the final mean squared error is returned as the score.
  
      >>> predict_stock_market_movement()
      2456.89
      """
output:     import pandas as pd
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      # Load the data
      data = pd.read_csv('data.csv')
      X = data.drop('target', axis=1).values
      y = data['target'].values
  
      # Build the model
      model = Sequential()
      model.add(Dense(64, input_dim=X.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(16, 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, y, epochs=20, batch_size=64, verbose=0)
  
      # Predict and return the mean squared error
      predictions = model.predict(X)
      mse = np.mean(np.square(predictions - y))
      return mse