input: Write a function to construct a neural network model using Keras in Python that correctly classifies the MNIST data set. The function should return the compiled Keras model.
  
  
  import keras
  
  def mnist_classifier():
      # 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
  
  assert isinstance(mnist_classifier(), keras.models.Model) == True
  
output: def mnist_classifier():
      input_layer = keras.layers.Input(shape=(784,))
      hidden_layer = keras.layers.Dense(128, activation='relu')(input_layer)
      output_layer = keras.layers.Dense(10, activation='softmax')(hidden_layer)
      model = keras.Model(inputs=input_layer, outputs=output_layer)
      model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      return model