input: Write a python function to create a Convolutional Neural Network model using TensorFlow and Keras.
  assert isinstance(create_model(), tf.keras.Sequential)
  assert len(create_model().layers) == 10
  assert isinstance(create_model().layers[0], tf.keras.layers.Conv2D)
output: def create_model():
      import tensorflow as tf
      from tensorflow.keras import layers, models
      
      model = models.Sequential()
      
      # First Convolutional Layer
      model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
      model.add(layers.MaxPooling2D((2, 2)))
      
      # Second Convolutional Layer
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      model.add(layers.MaxPooling2D((2, 2)))
      
      # Third Convolutional Layer
      model.add(layers.Conv2D(64, (3, 3), activation='relu'))
      
      # Flattening layer
      model.add(layers.Flatten())
      
      # Fully connected layer
      model.add(layers.Dense(64, activation='relu'))
      
      # Output layer
      model.add(layers.Dense(10, activation='softmax'))
      
      return model