input: Write a function to construct a neural network in Python that can perform basic regression. The function should load a dataset from a CSV file, split the data into training and testing sets, create a neural network model using Keras, compile and train the model, and then evaluate the model on the test data. The function should return the test score.
  assert perform_regression('data.csv') is not None
  assert perform_regression('non_existent_file.csv')==None
  assert perform_regression('empty_data.csv')==None
output: def perform_regression(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
      import os
  
      if not os.path.exists(file_path):
          return None
  
      try:
          data = pd.read_csv(file_path)
      except pd.errors.EmptyDataError:
          return None
  
      if data.empty:
          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, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(32, activation='relu'),
          Dense(1)
      ])
  
      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)
  
      return test_score