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 from a CSV file, split the data into training and testing sets, create a neural network model, compile the model, train it, and finally evaluate the model. The function should return the test score of the model. If the dataset is not applicable, the function should return None.
  
  
  assert construct_regression_model('data.csv') is not None
  assert construct_regression_model('nonexistent.csv') is None
  
output: def construct_regression_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      try:
          data = pd.read_csv(file_path)
      except FileNotFoundError:
          return None
  
      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)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      model = Sequential([
          Dense(64, input_dim=X_train.shape[1], activation='relu'),
          Dense(32, activation='relu'),
          Dense(1)
      ])
  
      model.compile(optimizer=Adam(), loss='mean_squared_error')
  
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
      test_score = model.evaluate(X_test, y_test, verbose=0)
  
      return test_score