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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def van(first_enc, first_frame, current_enc, gt_image, reuse=False, scope_prefix='', hparams=None):
 """Implements a VAN. Args: first_enc: The first encoding. first_frame: The first ground truth frame. current_enc: The encoding of the frame to generate. gt_image: The ground truth image, only used for regularization. reuse: To reuse in variable scope or not. scope_prefix: The prefix before the scope name. hparams: The python hparams. Returns: The generated image. """

  with tf.variable_scope(scope_prefix + 'van', reuse=reuse):
    output_shape = first_frame.get_shape().as_list()
    output_shape[0] = -1

    first_depth = 64

    f_first_enc, _ = van_enc_2d(first_enc, first_depth)
    f_first_frame, image_enc_history = van_image_enc_2d(
        first_frame, first_depth, hparams=hparams)
    f_current_enc, van_higher_level = van_enc_2d(
        current_enc, first_depth, reuse=True)
    f_gt_image, _ = van_image_enc_2d(gt_image, first_depth, True,
                                     hparams=hparams)

    analogy_t = analogy_computation_2d(
        f_first_enc, f_first_frame, f_current_enc, first_depth)
    enc_img = f_current_enc + analogy_t

    img = van_dec_2d(
        enc_img, image_enc_history, output_shape, first_depth, hparams=hparams)

    batch_size = tf.to_float(tf.shape(first_enc)[0])
    r_loss = tf.nn.l2_loss(f_gt_image - f_current_enc - analogy_t) / batch_size

    return img, r_loss, van_higher_level