input: Write a function to construct a neural network in Python that can perform basic regression. The function should load data from a CSV file, split the data into training and testing sets, create a neural network model using Keras, train the model, and evaluate it. The function should return the test score of the model.
  assert construct_regression_nn('data.csv')==(0.123)
  assert construct_regression_nn('data2.csv')==(0.089)
  assert construct_regression_nn('empty.csv')==(None)
  assert construct_regression_nn('incorrect_format.csv')==(None)
  assert construct_regression_nn('data_single_row.csv')==(None)
output: import pandas as pd
  import numpy as np
  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
  
  def construct_regression_nn(file_path):
      try:
          data = pd.read_csv(file_path)
          if data.shape[0] <= 1:
              return None
      except:
          return None
      
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      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()
      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=50, batch_size=32, verbose=0)
      
      test_score = model.evaluate(X_test, y_test, verbose=0)
      
      return test_score