input: Write a function that constructs a neural network using the Keras library for basic regression tasks. The function should load data from a CSV file, split the data into training and testing sets, create a neural network model with appropriate layers, compile the model, train it, and then evaluate it on the test set, printing the test score.
  assert perform_regression("data.csv") # This should print the test score
  assert perform_regression("another_data.csv") # This should print the test score
output: def perform_regression(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      data = pd.read_csv(csv_file)
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      test_score = model.evaluate(X_test, y_test, verbose=0)
      print(test_score)
      return test_score