code: def visualize_prediction(input_batch, pred_batch, gold_batch, tag):
      plt.figure()
      for i in range(1, 5):
          ax = plt.subplot(2, 2, i)
          ax.plot(
              np.concatenate(
                  [
                      input_batch[288 * (i - 1)],
                      gold_batch[288 * (i - 1)],
                  ]
              ),
              label="gold",
          )
          ax.plot(
              np.concatenate(
                  [
                      input_batch[288 * (i - 1)],
                      pred_batch[288 * (i - 1)],
                  ]
              ),
              label="pred",
          )
          ax.legend()
      plt.savefig(tag + "_vis.png")
      plt.close()
  
imports: import os
  import argparse
  import paddle
  import paddle.nn.functional
  import tqdm
  import yaml
  import numpy
  from easydict import EasyDict
  import pgl
  from pgl.utils.logger import log
  from paddle.io import DataLoader
  import random
  import loss
  from wpf_dataset import PGL4WPFDataset
  from wpf_model import WPFModel
  import optimization
  from metrics import regressor_scores, regressor_detailed_scores
  from utils import save_model, _create_if_not_exist, load_model
  import matplotlib
  import matplotlib.pyplot
label_desc: Use image node_id 3 for calling a global function with example usage: visualize_prediction(input_batch, pred_batch, gold_batch, tag) without return types