code: def main(argv):
      global DEBUG, DD_FORCE_LOAD, DASK_CLIENT
  
      parser = argparse.ArgumentParser(
          epilog=__doc__, formatter_class=argparse.RawTextHelpFormatter
      )
      parser.add_argument("filepath")
      parser.add_argument("dftype")
      parser.add_argument("base")
      parser.add_argument("x")
      parser.add_argument("y")
      parser.add_argument("categories", nargs="+")
      parser.add_argument(
          "--debug",
          action="store_true",
          help="Enable increased verbosity and DEBUG messages",
      )
      parser.add_argument(
          "--cache",
          choices=("persist", "cachey"),
          default=None,
          help='Enable caching: "persist" causes Dask dataframes to force loading into memory; "cachey" uses dask.cache.Cache with a cachesize of {}. Caching is disabled by default'.format(
              int(p.cachesize)
          ),
      )
      parser.add_argument(
          "--distributed",
          action="store_true",
          help="Enable the distributed scheduler instead of the threaded, which is the default.",
      )
      parser.add_argument(
          "--recalc-ranges",
          action="store_true",
          help="Tell datashader to recalculate the ranges on each aggregation, instead of caching them (by default).",
      )
      args = parser.parse_args(argv[1:])
  
      if args.cache is None:
          if args.debug:
              print("DEBUG: Cache disabled", flush=True)
      else:
          if args.cache == "cachey":
              from dask.cache import Cache
  
              cache = Cache(p.cachesize)
              cache.register()
          elif args.cache == "persist":
              DD_FORCE_LOAD = True
  
          if args.debug:
              print(
                  'DEBUG: Cache "{}" mode enabled'.format(args.cache),
                  flush=True,
              )
  
      if args.dftype == "dask" and args.distributed:
          local_cluster = distributed.LocalCluster(
              n_workers=p.n_workers, threads_per_worker=1
          )
          DASK_CLIENT = distributed.Client(local_cluster)
          if args.debug:
              print('DEBUG: "distributed" scheduler is enabled')
      else:
          if args.dftype != "dask" and args.distributed:
              raise ValueError(
                  "--distributed argument is only available with the dask dataframe type (not pandas)"
              )
          if args.debug:
              print('DEBUG: "threaded" scheduler is enabled')
  
      filepath = args.filepath
      basename, extension = os.path.splitext(filepath)
      p.dftype = args.dftype
      p.base = args.base
      p.x = args.x
      p.y = args.y
      p.categories = args.categories
      DEBUG = args.debug
  
      if DEBUG:
          print(
              "DEBUG: Memory usage (before read):\t{} MB".format(
                  get_proc_mem()
              ),
              flush=True,
          )
      df, loadtime = timed_read(filepath, p.dftype)
  
      if df is None:
          if loadtime == -1:
              print(
                  "{:28} {:6}  Operation not supported".format(
                      filepath, p.dftype
                  ),
                  flush=True,
              )
          return 1
  
      if DEBUG:
          print(
              "DEBUG: Memory usage (after read):\t{} MB".format(
                  get_proc_mem()
              ),
              flush=True,
          )
  
      img, aggtime1 = timed_agg(
          df, filepath, 5, 5, cache_ranges=(not args.recalc_ranges)
      )
      if DEBUG:
          mem_usage = df.memory_usage(deep=True)
          if p.dftype == "dask":
              mem_usage = mem_usage.compute()
          print("DEBUG:", mem_usage, flush=True)
          mem_usage_total = mem_usage.sum()
          print(
              "DEBUG: DataFrame size:\t\t\t{} MB".format(
                  mem_usage_total / 1e6
              ),
              flush=True,
          )
          for colname in df.columns:
              print(
                  'DEBUG: column "{}" dtype: {}'.format(
                      colname, df[colname].dtype
                  )
              )
          print(
              "DEBUG: Memory usage (after agg1):\t{} MB".format(
                  get_proc_mem()
              ),
              flush=True,
          )
  
      img, aggtime2 = timed_agg(
          df, filepath, cache_ranges=(not args.recalc_ranges)
      )
      if DEBUG:
          print(
              "DEBUG: Memory usage (after agg2):\t{} MB".format(
                  get_proc_mem()
              ),
              flush=True,
          )
  
      in_size = get_size(filepath)
      out_size = get_size(filepath + ".png")
  
      global_end = time.time()
      print(
          "{:28} {:6}  Aggregate1:{:06.2f} ({:06.2f}+{:06.2f})  Aggregate2:{:06.2f}  In:{:011d}  Out:{:011d}  Total:{:06.2f}".format(
              filepath,
              p.dftype,
              loadtime + aggtime1,
              loadtime,
              aggtime1,
              aggtime2,
              in_size,
              out_size,
              global_end - global_start,
          ),
          flush=True,
      )
  
      return 0
  
imports: from __future__ import annotations
  import time
  import os, os.path, sys, glob, argparse, resource, multiprocessing
  import pandas
  import dask.dataframe
  import numpy
  import datashader
  import feather
  import fastparquet
  from datashader.utils import export_image
  from datashader import transfer_functions
  from dask import distributed
label_desc: Use image node_id 12 for calling a global function with example usage: main(argv) and returns: int, int