code: def group_filter_handler(col_def, group_val, group_classifier):
      col_def_segs = col_def.split(CHART_JOINER_CHAR)
      if len(col_def_segs) > 1:
          col, freq = col_def_segs
          if group_val == "NaN":
              return (
                  "{col} != {col}".format(col=build_col_key(col)),
                  "{}: NaN".format(col),
              )
          if freq == "WD":
              return (
                  "{}.dt.dayofweek == {}".format(
                      build_col_key(col), group_val
                  ),
                  "{}.dt.dayofweek: {}".format(col, group_val),
              )
          elif freq == "H2":
              return (
                  "{}.dt.hour == {}".format(
                      build_col_key(col), group_val
                  ),
                  "{}.dt.hour: {}".format(col, group_val),
              )
          elif freq == "H":
              ts_val = pd.Timestamp(group_val)
              day = ts_val.strftime("%Y%m%d")
              hour = ts_val.hour
              return (
                  "{col}.dt.date == '{day}' and {col}.dt.hour == {hour}".format(
                      col=build_col_key(col), day=day, hour=hour
                  ),
                  "{col}.dt.date: {day}, {col}.dt.hour: {hour}".format(
                      col=col, day=day, hour=hour
                  ),
              )
          elif freq == "D":
              ts_val = convert_date_val_to_date(group_val)
              day = ts_val.strftime("%Y%m%d")
              return (
                  "{col}.dt.date == '{day}'".format(
                      col=build_col_key(col), day=day
                  ),
                  "{}.dt.date: {}".format(col, day),
              )
          elif freq == "W":
              ts_val = convert_date_val_to_date(group_val)
              return (
                  "{col}.dt.year == {year} and {col}.dt.week == {week}".format(
                      col=build_col_key(col),
                      year=ts_val.year,
                      week=ts_val.week,
                  ),
                  "{col}.dt.year: {year}, {col}.dt.week: {week}".format(
                      col=col, year=ts_val.year, week=ts_val.week
                  ),
              )
          elif freq == "M":
              ts_val = convert_date_val_to_date(group_val)
              return (
                  "{col}.dt.year == {year} and {col}.dt.month == {month}".format(
                      col=build_col_key(col),
                      year=ts_val.year,
                      month=ts_val.month,
                  ),
                  "{col}.dt.year: {year}, {col}.dt.month: {month}".format(
                      col=col, year=ts_val.year, month=ts_val.month
                  ),
              )
          elif freq == "Q":
              ts_val = convert_date_val_to_date(group_val)
              return (
                  "{col}.dt.year == {year} and {col}.dt.quarter == {quarter}".format(
                      col=build_col_key(col),
                      year=ts_val.year,
                      quarter=ts_val.quarter,
                  ),
                  "{col}.dt.year: {year}, {col}.dt.quarter: {quarter}".format(
                      col=col, year=ts_val.year, quarter=ts_val.quarter
                  ),
              )
          elif freq == "Y":
              ts_val = convert_date_val_to_date(group_val)
              return (
                  "{col}.dt.year == {year}".format(
                      col=build_col_key(col), year=ts_val.year
                  ),
                  "{}.dt.year: {}".format(col, ts_val.year),
              )
      if group_val == "NaN":
          return (
              "{col} != {col}".format(col=build_col_key(col_def)),
              "{}: NaN".format(col_def),
          )
      if group_classifier in ["I", "F", "B"]:
          return (
              "{col} == {val}".format(
                  col=build_col_key(col_def),
                  val="{}".format(group_val).replace(",", ""),
              ),
              "{}: {}".format(col_def, group_val),
          )
      if group_classifier == "D":
          group_val = convert_date_val_to_date(group_val).strftime(
              "%Y%m%d"
          )
      return (
          "{col} == '{val}'".format(
              col=build_col_key(col_def), val=group_val
          ),
          "{}: {}".format(col_def, group_val),
      )
  
imports: import copy
  import numpy
  import pandas
  import dtale.global_state
  import dtale.pandas_util
  from dtale.column_analysis import handle_cleaners
  from dtale.constants import CHART_JOINER_CHAR
  from dtale.query import build_col_key, run_query
  from dtale.utils import ChartBuildingError, build_formatters, classify_type, find_dtype, find_dtype_formatter, flatten_lists, get_dtypes, json_int, make_list, triple_quote
label_desc: Use image node_id 6 for calling a global function with example usage: group_filter_handler(col_def, group_val, group_classifier) and returns: str,  str, str,  str, str,  str, str,  str, str,  str, str,  str, str,  str, str,  str, str,  str, str,  str, str,  str, str,  str