input: from typing import List, Dict
  
  def predict_stock_market_movement(data_path: str, epochs: int, batch_size: int) -> Dict:
      """
      Predicts stock market movements using a deep learning model. The function takes the path
      to a CSV file containing the data, the number of epochs, and the batch size as inputs.
      It splits the data into training and testing sets, builds and trains a deep learning model,
      and returns a dictionary containing the model's evaluation score.
      
      >>> predict_stock_market_movement('data.csv', 20, 64)
      {'mse': 0.01, 'model': <keras.engine.sequential.Sequential object at 0x7f9a5b0f1c10>}
      >>> predict_stock_market_movement('data2.csv', 10, 32)
      {'mse': 0.015, 'model': <keras.engine.sequential.Sequential object at 0x7f9a5b0f1d30>}
      """
      
      #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
  
      #Build a deep learning model
      #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 {'mse': score[0], 'model': model}
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
  
      #Build a deep learning model
      #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 {'mse': score[0], 'model': model}