input: def recognize_handwritten_digits(X_train: np.ndarray, y_train: np.ndarray, X_test: np.ndarray, y_test: np.ndarray) -> Model:
      """Create and train a Machine Learning model using a neural network to recognize handwritten digits from 0 to 9.
      The model uses the MNIST dataset for training and testing.
      
      >>> X_train, y_train, X_test, y_test = mnist.load_data()
      >>> model = recognize_handwritten_digits(X_train, y_train, X_test, y_test)
      >>> model.evaluate(X_test, y_test)
      [0.08, 0.97]
      """
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.layers import Dense, Input, Dropout
      from tensorflow.keras.models import Model
      from tensorflow.keras.utils import to_categorical
  
      # Reshape inputs from (60000, 28, 28) to (60000, 784)
      X_train = X_train.reshape(60000, 784)
      X_test = X_test.reshape(10000, 784)
  
      # Normalize pixel values
      X_train = X_train.astype('float32')
      X_train /= 255
      X_test = X_test.astype('float32')
      X_test /= 255
  
      # One-hot encode labels
      y_train = to_categorical(y_train, num_classes=10)
      y_test = to_categorical(y_test, num_classes=10)
  
      # Input layer
      inputs = Input(shape=(784,))
  
      # Hidden layer
      h1 = Dense(64, activation='relu')(inputs)
      h2 = Dropout(0.5)(h1)
  
      # Output layer
      outputs = Dense(10, activation='softmax')(h2)
  
      # Compiling the model
      model = Model(inputs=inputs, outputs=outputs)
      model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  
      # Fitting the model
      model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=128)
  
      return model
output:     return model