input: from typing import List
  
  
  def perform_regression(path: str, epochs: int, batch_size: int) -> float:
      """ Construct a neural network in Python that performs basic regression on data from a given path.
      The function should load the data, split it into features and labels, create a neural network, 
      compile it, train it for the specified number of epochs and batch size, and finally, evaluate the model.
      
      >>> perform_regression('data.csv', 100, 20)
      0.123456
      """
      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(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=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=epochs, batch_size=batch_size, verbose=0)
  
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score
output:     return score