code: def is_eval_epoch(cfg, cur_epoch, multigrid_schedule):
      """
      Determine if the model should be evaluated at the current epoch.
      Args:
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
          cur_epoch (int): current epoch.
          multigrid_schedule (List): schedule for multigrid training.
      """
      if cur_epoch + 1 == cfg.SOLVER.MAX_EPOCH:
          return True
      if multigrid_schedule is not None:
          prev_epoch = 0
          for s in multigrid_schedule:
              if cur_epoch < s[-1]:
                  period = max(
                      (s[-1] - prev_epoch) // cfg.MULTIGRID.EVAL_FREQ
                      + 1,
                      1,
                  )
                  return (s[-1] - 1 - cur_epoch) % period == 0
              prev_epoch = s[-1]
  
      return (cur_epoch + 1) % cfg.TRAIN.EVAL_PERIOD == 0
  
imports: import json
  import logging
  import math
  import numpy
  import os
  from datetime import datetime
  import psutil
  import torch
  import torchvision.io
  from fvcore.nn.activation_count import activation_count
  from fvcore.nn.flop_count import flop_count
  from matplotlib import pyplot
  from torch import nn
  from torchvision.utils import make_grid
  import slowfast.utils.logging
  import slowfast.utils.multiprocessing
  from slowfast.datasets.utils import pack_pathway_output
  from slowfast.models.batchnorm_helper import SubBatchNorm3d
  from slowfast.utils.env import pathmgr
label_desc: Use image node_id 8 for calling a global function with example usage: is_eval_epoch(cfg, cur_epoch, multigrid_schedule) and returns: unknown, True, unknown