input: from typing import List, Dict
  
  
  def predict_stock_market(data: pd.DataFrame) -> Dict[str, float]:
      """
      This function takes a DataFrame containing stock market data and returns a dictionary with predicted stock market movements.
      The function uses a deep learning model built with TensorFlow to make predictions.
      
      >>> predict_stock_market(pd.DataFrame({'X': [1.0, 2.0, 3.0], 'Y': [4.0, 5.0, 6.0]}))
      {'prediction_1': 6.5, 'prediction_2': 7.8}
      """
output:     
      import tensorflow as tf
      from sklearn.preprocessing import StandardScaler
  
      # Prepare the data for the model
      X = data[['X']].values
      Y = data[['Y']].values
  
      # Scale the data
      scaler = StandardScaler()
      X_scaled = scaler.fit_transform(X)
      Y_scaled = scaler.fit_transform(Y)
  
      # Build the model
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(10, activation='relu', input_shape=(1,)),
          tf.keras.layers.Dense(1)
      ])
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_scaled, Y_scaled, epochs=10, verbose=0)
  
      # Make predictions
      predictions = model.predict(X_scaled)
  
      # Inverse transform the predictions to get the original scale
      predictions = scaler.inverse_transform(predictions)
  
      # Return the predictions as a dictionary
      return {'prediction_1': predictions[0][0], 'prediction_2': predictions[1][0]}
  