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 include data loading, model definition, model compilation, data pre-processing, and model training steps. Return the trained model.
  
  Line 1: assert type(ML())==Model
output: def ML():
      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'])
      model.fit(x_train, y_train, epochs=5, batch_size=32, validation_data=(x_test, y_test))
  
      return model