input: 
  def predict_stock_market(data_path: str) -> float:
      """
      Predicts the stock market movements using an AI-based deep learning model.
      The function takes the path to a CSV file containing stock market data, splits it into training and testing sets,
      builds a neural network model, trains it, and then evaluates its performance.
      The function returns the evaluation score of the model.
  
      >>> predict_stock_market('data.csv')
      2500.5
      """
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      import seaborn as sns
      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 score[0]
  
output:     import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      import seaborn as sns
      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 score[0]