code: def ensure_benchmark_data(
      symbol, first_date, last_date, now, trading_day, environ=None
  ):
      """
      Ensure we have benchmark data for `symbol` from `first_date` to `last_date`
  
      Parameters
      ----------
      symbol : str
          The symbol for the benchmark to load.
      first_date : pd.Timestamp
          First required date for the cache.
      last_date : pd.Timestamp
          Last required date for the cache.
      now : pd.Timestamp
          The current time.  This is used to prevent repeated attempts to
          re-download data that isn't available due to scheduling quirks or other
          failures.
      trading_day : pd.CustomBusinessDay
          A trading day delta.  Used to find the day before first_date so we can
          get the close of the day prior to first_date.
  
      We attempt to download data unless we already have data stored at the data
      cache for `symbol` whose first entry is before or on `first_date` and whose
      last entry is on or after `last_date`.
  
      If we perform a download and the cache criteria are not satisfied, we wait
      at least one hour before attempting a redownload.  This is determined by
      comparing the current time to the result of os.path.getmtime on the cache
      path.
      """
      filename = get_benchmark_filename(symbol)
      data = _load_cached_data(
          filename, first_date, last_date, now, "benchmark", environ
      )
      if data is not None:
          return data
  
      # If no cached data was found or it was missing any dates then download the
      # necessary data.
      logger.info(
          (
              "Downloading benchmark data for {symbol!r} "
              "from {first_date} to {last_date}"
          ),
          symbol=symbol,
          first_date=first_date - trading_day,
          last_date=last_date,
      )
  
      try:
          data = get_benchmark_returns(
              symbol,
              first_date - trading_day,
              last_date,
          )
          data.to_csv(get_data_filepath(filename, environ))
      except (OSError, IOError, HTTPError):
          logger.exception("Failed to cache the new benchmark returns")
          raise
      if not has_data_for_dates(data, first_date, last_date):
          logger.warn(
              "Still don't have expected data after redownload!"
          )
      return data
  
imports: import os
  from collections import OrderedDict
  import logbook
  import pandas
  import pytz
  from pandas_datareader.data import DataReader
  from six import iteritems
  from six.moves.urllib_error import HTTPError
  from catalyst.constants import LOG_LEVEL
  from catalyst.utils.calendars import get_calendar
  from .None import treasuries, treasuries_can
  from .benchmarks import get_benchmark_returns
  from ..utils.deprecate import deprecated
  from ..utils.paths import cache_root, data_root
label_desc: Use image node_id 9 for calling a global function with example usage: ensure_benchmark_data(symbol, first_date, last_date, now, trading_day, environ) and returns: data, data