code: def _abnormal_value_check(df, dt_col, threshold=10):
      """
      This check is used to determine whether there are abnormal values in the data.
      """
      for column in df.columns:
          if column == dt_col or pd.api.types.is_string_dtype(
              df[column]
          ):
              continue
          df_col = df[column]
          std_val = df_col.std()
          mean_val = df_col.mean()
          df_col = df_col.apply(lambda x: x - mean_val)
          if (
              df_col.max() > std_val * threshold
              or df_col.min() < -std_val * threshold
          ):
              logging.warning(
                  f"Some values of column {column} exceeds the mean plus/minus "
                  f"{threshold} times standard deviation, please call "
                  f".repair_abnormal_data() to remove abnormal values."
              )
              return False
      return True
  
file_path: BigDL/python/chronos/src/bigdl/chronos/data/utils/quality_inspection.py
imports: import pandas
  import numpy
  from bigdl.nano.utils.common import invalidInputError
  import logging
label_desc: Use image node_id 8 for calling a global function with example usage: _abnormal_value_check(df, dt_col, threshold) and returns: True, False