input: 
  from typing import List
  
  def predict_stock_movements(data_path: str, epochs: int, batch_size: int) -> float:
      """ Predicts stock market movements using a deep learning model. The model is trained on
      historical stock data and can predict future stock prices. The function returns the mean
      squared error of the model's predictions on the test data.
      >>> predict_stock_movements('data.csv', 20, 64)
      0.0345
      """
      #import libraries
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      #import data
      data = pd.read_csv(data_path)
  
      #split data into train and test data
      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
  
      #create a model and add layers
      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))
  
      #compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      #train the model
      model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))
  
      #Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[1]
  
output:     #import libraries
      import pandas as pd
      import numpy as np
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      #import data
      data = pd.read_csv(data_path)
  
      #split data into train and test data
      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
  
      #create a model and add layers
      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))
  
      #compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      #train the model
      model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))
  
      #Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score[1]