input: def recognize_handwritten_digits() -> Model:
      """
      This function generates a Machine Learning model in Python which can recognize handwritten digits from 0 to 9.
      The model uses a neural network with an input layer, a hidden layer with dropout, and an output layer. 
      The model is trained on the MNIST dataset.
      
      Example:
      >>> from tensorflow.keras.models import Model
      >>> model = recognize_handwritten_digits()
      >>> isinstance(model, Model)
      True
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Dropout, Flatten
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      # Load the MNIST dataset
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
      # Preprocess the data
      x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype('float32') / 255
      x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype('float32') / 255
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
  
      # Build the model
      model = Sequential()
      model.add(Flatten(input_shape=(28, 28, 1)))
      model.add(Dense(128, activation='relu'))
      model.add(Dropout(0.2))
      model.add(Dense(10, activation='softmax'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(x_train, y_train, epochs=5, batch_size=32, validation_data=(x_test, y_test))
  
      return model