input: 
  def stock_prediction_model(data_path: str) -> str:
      """Given a path to a CSV file containing stock market data, build and train a deep learning model to predict stock market movements. 
      The function should return a string representation of the evaluation score.
  
      >>> stock_prediction_model('data.csv')
      '[0.123, 0.123]'
      """
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      data = pd.read_csv(data_path) 
      X_train=data.iloc[:, 0] 
      y_train=data.iloc[:, 1]  
      X_test=data.iloc[:, 0] 
      y_test=data.iloc[:, 1]  
  
      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 str(score)
  
  
output:     import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      data = pd.read_csv(data_path) 
      X_train=data.iloc[:, 0] 
      y_train=data.iloc[:, 1]  
      X_test=data.iloc[:, 0] 
      y_test=data.iloc[:, 1]  
  
      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 str(score)