input: 
  from typing import List
  
  def construct_mnist_model() -> keras.Model:
      """ Constructs a neural network model using Keras to classify the MNIST data set.
      The model includes:
      1. An input layer with 784 features.
      2. A hidden layer with 128 units using ReLU activation.
      3. An output layer with 10 units using softmax activation for classification.
      The model is compiled using the Adam optimizer, sparse categorical crossentropy loss,
      and accuracy as the metric.
      >>> model = construct_mnist_model()
      >>> 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:     
      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
  