input: 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
  
  def build_regression_model(data_path: str, test_size: float = 0.2, epochs: int = 100, batch_size: int = 20) -> float:
      """Construct a neural network in Python that can perform basic regression on a dataset loaded from a CSV file.
      The function returns the test score of the model after evaluation.
      >>> build_regression_model('data.csv')
      'Test score: 0.0534'  # Example output, actual value will depend on the dataset
      """
      # Load the data
      data = pd.read_csv(data_path)
       
      # 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=test_size, 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=epochs, batch_size=batch_size, verbose=0)
       
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return f'Test score: {score}'
output:     # Load the data
      data = pd.read_csv(data_path)
       
      # 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=test_size, 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=epochs, batch_size=batch_size, verbose=0)
       
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return f'Test score: {score}'