input: 
  def construct_neural_network():
      """
      Constructs and trains a neural network in Python that can perform basic regression.
      The neural network is built using Keras with TensorFlow backend. It consists of three fully connected layers,
      with the first two layers having ReLU activation functions and the last layer having no activation function since
      it is a regression task. The model is compiled using the mean squared error loss function and the Adam optimizer.
      The model is then trained on a dataset loaded from 'data.csv'.
  
      Example:
      >>> construct_neural_network()
      Test score: 0.05
      """
      import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from keras.models import Sequential
      from keras.layers import Dense
      
      # Load the data
      data = pd.read_csv('data.csv')
      
      # Split the data into features and labels
      X = data.drop(['label'], axis=1).values
      y = data['label'].values
      
      # Split the data 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 the neural network
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
      model.add(Dense(12, kernel_initializer='normal', activation='relu'))
      model.add(Dense(1, kernel_initializer='normal'))
      
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
      
      # Train the model
      model.fit(X_train, y_train, epochs=100, batch_size=20, verbose=0)
      
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      print('Test score:', score)
  
output:     return model