input: from typing import List
  
  def construct_mnist_classifier() -> keras.Model:
      """ Construct a neural network model using Keras to classify the MNIST data set correctly.
      The model should consist of:
      1. An input layer accepting data of shape (784,).
      2. A hidden layer with 128 units and ReLU activation.
      3. An output layer with 10 units and softmax activation.
      4. The model should be compiled using the Adam optimizer, sparse categorical crossentropy loss,
      and accuracy as the metric.
      >>> model = construct_mnist_classifier()
      >>> model.input_shape == (None, 784)
      True
      >>> model.output_shape == (None, 10)
      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:     import keras