input: from typing import List
  
  def construct_neural_network_mnist() -> keras.Model:
      """
      Construct a neural network model in Python using Keras to classify the MNIST data set correctly.
      The network should include an input layer, a hidden layer with ReLU activation, and an output
      layer with softmax activation. The model should be compiled with the Adam optimizer and 
      sparse categorical cross-entropy loss.
  
      Example:
      >>> model = construct_neural_network_mnist()
      >>> isinstance(model, keras.Model)
      True
      """
      import keras
  
      # Input layer
      input_layer = keras.layers.Input(shape=(784,))
  
      # Hidden layer
      hidden_layer = keras.layers.Dense(128, 
                      activation='relu')(input_layer)
  
      # Output layer
      output_layer = keras.layers.Dense(10, 
                      activation='softmax')(hidden_layer)
  
      # Model
      model = keras.Model(inputs=input_layer, outputs=output_layer)
  
      # Compile model
      model.compile(optimizer='adam', 
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])
      
      return model
output:     
      return model
  