code: def __call__(self, df_features):
      if "date" in df_features:
          df_features.droplevel("date", inplace=True)
      df_values = df_features.values
      index = 0
      names = {}
      for name, dim in self.norm_groups.items():
          names[name] = slice(index, index + dim)
          index += dim
      for name, name_val in names.items():
          feature_mean = np.load(
              self.feature_save_dir + name + "_mean.npy"
          )
          feature_std = np.load(
              self.feature_save_dir + name + "_std.npy"
          )
  
          if name.endswith("volume"):
              df_values[:, name_val] = np.log1p(df_values[:, name_val])
          df_values[:, name_val] -= feature_mean
          df_values[:, name_val] /= feature_std
      df_features = pd.DataFrame(
          data=df_values,
          index=df_features.index,
          columns=df_features.columns,
      )
      return df_features.fillna(0)
  
file_path: qlib/qlib/contrib/data/highfreq_processor.py
imports: import os
  import numpy
  import pandas
  from qlib.data.dataset.processor import Processor
  from qlib.data.dataset.utils import fetch_df_by_index
  from typing import Dict
label_desc: Use image node_id 3 for calling the HighFreqNorm obj's underlying member method code with example usage: obj.__call__(df_features) and returns: df_features