input: Write a function to create a Machine Learning model in Python that can recognize handwritten digits from 0 to 9. The function should load the MNIST dataset, define a neural network model with appropriate layers, compile the model, preprocess the data, and fit the model to the training data.
  assert recognize_handwritten_digits().history['accuracy'][-1] > 0.9
  assert recognize_handwritten_digits().history['val_accuracy'][-1] > 0.9
output: 
  def recognize_handwritten_digits():
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense, Flatten
      from tensorflow.keras.utils import to_categorical
      from tensorflow.keras.optimizers import Adam
  
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.reshape(-1, 28*28) / 255.0
      x_test = x_test.reshape(-1, 28*28) / 255.0
      y_train = to_categorical(y_train, 10)
      y_test = to_categorical(y_test, 10)
  
      model = Sequential([
          Flatten(input_shape=(28*28,)),
          Dense(128, activation='relu'),
          Dense(64, activation='relu'),
          Dense(10, activation='softmax')
      ])
  
      model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
      history = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, batch_size=32)
  
      return history
  