code: def data_gen_mydata(
      input_file, label_file, n, n_his, n_pred, n_config
  ):
      """data processing"""
      # data
      x = pd.read_csv(input_file)
      y = pd.read_csv(label_file)
      x = x.drop(columns=["date"])
      y = y.drop(columns=["date"])
  
      # param
      n_val, n_test = n_config
      n_train = len(y) - n_val - n_test - 2
  
      # (?,26,74,1)
      df = pd.DataFrame(columns=x.columns)
      for i in range(len(y) - n_pred + 1):
          df = df.append(x[i : i + n_his])
          df = df.append(y[i : i + n_pred])
  
      data = df.values.reshape(-1, n_his + n_pred, n, 1)
  
      x_stats = {"mean": np.mean(data), "std": np.std(data)}
  
      x_train = data[:n_train]
      x_val = data[n_train : n_train + n_val]
      x_test = data[n_train + n_val :]
  
      x_data = {"train": x_train, "val": x_val, "test": x_test}
      dataset = Dataset(x_data, x_stats)
      return dataset
  
file_path: PGL/legacy/examples/stgcn/data_loader/data_utils.py
imports: import numpy
  import pandas
  from utils.math_utils import z_score
label_desc: Use image node_id 4 for calling a global function with example usage: data_gen_mydata(input_file, label_file, n, n_his, n_pred, n_config) and returns: dataset