code: def load_test_data(
      filename, ts_column, sep, target, dask_xgboost_flag=0
  ):
      """
      This function loads a given filename into a pandas dataframe and sets the
      ts_column as a Time Series index. Note that filename should contain the full
      path to the file.
      """
  
      if isinstance(filename, str):
          filename = pd.read_csv(
              filename, sep=sep, index_col=ts_column, parse_dates=True
          )
          ### If filename is not a string, it must be a dataframe and can be loaded
      else:
          if type(filename) == dask.dataframe.core.DataFrame:
              print(
                  "    Since dask_xgboost_flag is True, and input is dask, continuing..."
              )
              ddf = filename.compute()
              print(
                  "    Converted dask dataframe into a pandas dataframe ..."
              )
              print(
                  "    Reducing memory size of df and loading into dask"
              )
              dft = reduce_mem_usage(ddf)
          else:
              dft = copy.deepcopy(filename)
              print("    Using given input: pandas dataframe...")
      ##################    L O A D    T E S T   D A T A      ######################
      dft = remove_duplicate_cols_in_dataset(dft)
      return dft
  
imports: from typing import List
  import numpy
  import pandas
  import copy
  import pdb
  from sklearn.model_selection import TimeSeriesSplit
  import dask
  import dask.dataframe
  import datetime
  from datetime import datetime, date, time
label_desc: Use image node_id 2 for calling a global function with example usage: load_test_data(filename, ts_column, sep, target, dask_xgboost_flag) and returns: dft