input: Write a function to construct a neural network model in Python using Keras to classify the MNIST dataset correctly. The model should include an input layer, a hidden layer, and an output layer. The input layer should have a shape of (784,) and the output layer should have a shape of (10,) with a softmax activation function.
  
  def mnist_classifier():
      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
  
  assert isinstance(mnist_classifier(), keras.Model)
output: def mnist_classifier():
      import keras
  
      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