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Solve the following python programming problem given the following code:
# Getting input data
def create_dataset(num_full_images):
  def generate_patches():
    for i in range(num_full_images):
      img, label = generate_image()
      patches, patch_labels = get_patches(img, label)
      # print(len(patches) * num_full_images)
      for patch, patch_label in zip(patches, patch_labels):
        yield patch, patch_label

  return tf.data.Dataset.from_generator(
    generate_patches,
    (tf.float32, tf.float32), # patch, patch_label
    (tf.TensorShape([INPUT_HEIGHT*INPUT_WIDTH*IMG_CHANNELS]),
    tf.TensorShape([]))  
  )

trainds = create_dataset(1) # will create 256 patches per image
for img, label in trainds.take(3):
  avg = tf.math.reduce_mean(img) # avg pixel in image
  print(img.shape, label.numpy(), avg.numpy())

# Train
NUM_TRAIN = 200 # 10000 more realistic
NUM_EVAL = 10 # 1000 more realistic
NUM_EPOCHS = 5

def training_plot(metrics, history):
  f, ax = plt.subplots(1, len(metrics), figsize=(5*len(metrics), 5))
  for idx, metric in enumerate(metrics):
    ax[idx].plot(history.history[metric], ls='dashed')
    ax[idx].set_xlabel("Epochs")
    ax[idx].set_ylabel(metric)
    ax[idx].plot(history.history['val_' + metric]);
    ax[idx].legend([metric, 'val_' + metric])

def train_and_evaluate(batch_size = 32,
                       lrate = 0.001, # default in Adam constructor
                       l1 = 0,
                       l2 = 0,
                       num_filters = 32):
  regularizer = tf.keras.regularizers.l1_l2(l1, l2)
  train_dataset = create_dataset(NUM_TRAIN).batch(batch_size)
  eval_dataset = create_dataset(NUM_EVAL).batch(64)

  # a simple convnet. you can make it more complex, of course
  # the patch is flattened, so we start by reshaping to an image
  model = tf.keras.Sequential([
              tf.keras.layers.Reshape([INPUT_HEIGHT, INPUT_WIDTH, IMG_CHANNELS],
                                      input_shape=[INPUT_WIDTH * INPUT_HEIGHT * IMG_CHANNELS]),
              tf.keras.layers.Conv2D(num_filters, (3,3), activation='relu'),
             ...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Train a regression model to predict density