input: Write a function to construct a neural network in Python that can perform basic regression on a given dataset. The function should load the dataset, split it into training and testing sets, create a neural network model using Keras, compile and train the model, and finally evaluate the model on the test set. Return the test score as the output of the function.
  assert construct_regression_model('data.csv') is not None
output: def construct_regression_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      # Load the dataset
      data = pd.read_csv(file_path)
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Create a neural network model
      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'))
  
      # Compile the model
      model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
      # Evaluate the model on the test set
      test_score = model.evaluate(X_test, y_test, verbose=0)
  
      return test_score