input: def build_regression_neural_network(data_filename: str) -> Sequential:
      """Construct a neural network in Python that can perform basic regression on dataset from data_filename. The neural network should have three Dense layers, with the first two layers having 12 units and using ReLU activation function and the last layer with 1 unit for regression. The model should be compiled with mean squared error as loss function and Adam as optimizer. It should be trained for 100 epochs with a batch size of 20.
      >>> build_regression_neural_network('dummy_data.csv')
      <keras.engine.sequential.Sequential object at 0x...>
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      import pandas as pd
  
      # Load the dataset
      data = pd.read_csv(data_filename)
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Build the model
      model = Sequential()
      model.add(Dense(12, input_dim=X.shape[1], activation='relu'))
      model.add(Dense(12, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Train the model
      model.fit(X, y, epochs=100, batch_size=20, verbose=0)
  
      return model