input: 
  def construct_regression_neural_network_from_csv() -> Sequential:
      """ Construct a neural network in Python that can perform basic regression using data from a CSV file.
      The network should have an architecture suitable for regression tasks, including an input layer with nodes
      corresponding to the number of features in the data, hidden layers with ReLU activation, and an output layer
      with a single node for regression output.
      The CSV file should contain a column named 'label' which represents the target variable.
      >>> model = construct_regression_neural_network_from_csv()
      >>> type(model)
      <class 'keras.engine.sequential.Sequential'>
      """
  
output:     import pandas as pd
      from keras.models import Sequential
      from keras.layers import Dense
  
      # Load the CSV file
      data = pd.read_csv('data.csv')
  
      # Extract the number of features (excluding the 'label' column)
      num_features = data.shape[1] - 1
  
      # Define the model
      model = Sequential()
      model.add(Dense(64, input_dim=num_features, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      return model