input: Write a function to create and train a Machine Learning model in Python that can recognize handwritten digits from 0 to 9 using the MNIST dataset. The function should load the dataset, define a neural network model with appropriate layers, preprocess the data, compile the model, and fit it on the training data. Return the trained model.
  assert type(create_digit_recognition_model())==Model
  assert create_digit_recognition_model().history is not None
  assert len(create_digit_recognition_model().layers) >= 3
output: 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
  
  def create_digit_recognition_model():
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train / 255.0
      x_test = x_test / 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'])
      model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))
      
      return model