input: Write a function to generate a Python code that creates a Machine Learning model using TensorFlow and Keras to recognize handwritten digits from 0 to 9. The model should include data loading, preprocessing, and training steps. If the code is successfully generated and runs without errors, return True; otherwise, return False. If the input is empty (meaning no code provided to process), return False.
  assert generate_model()==True
  assert generate_model_without_code()==False
output: def generate_model():
      try:
          import tensorflow as tf
          from tensorflow.keras import datasets, layers, models
  
          (train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
          train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
          test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255
  
          model = models.Sequential([
              layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
              layers.MaxPooling2D((2, 2)),
              layers.Conv2D(64, (3, 3), activation='relu'),
              layers.MaxPooling2D((2, 2)),
              layers.Conv2D(64, (3, 3), activation='relu'),
              layers.Flatten(),
              layers.Dense(64, activation='relu'),
              layers.Dense(10, activation='softmax')
          ])
  
          model.compile(optimizer='adam',
                        loss='sparse_categorical_crossentropy',
                        metrics=['accuracy'])
  
          model.fit(train_images, train_labels, epochs=5)
          return True
      except:
          return False
  
  def generate_model_without_code():
      return False