code: def get_model_stats(model, cfg, mode, use_train_input):
      """
      Compute statistics for the current model given the config.
      Args:
          model (model): model to perform analysis.
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
          mode (str): Options include `flop` or `activation`. Compute either flop
              (gflops) or activation count (mega).
          use_train_input (bool): if True, compute statistics for training. Otherwise,
              compute statistics for testing.
  
      Returns:
          float: the total number of count of the given model.
      """
      assert mode in [
          "flop",
          "activation",
      ], "'{}' not supported for model analysis".format(mode)
      if mode == "flop":
          model_stats_fun = flop_count
      elif mode == "activation":
          model_stats_fun = activation_count
  
      # Set model to evaluation mode for analysis.
      # Evaluation mode can avoid getting stuck with sync batchnorm.
      model_mode = model.training
      model.eval()
      inputs = _get_model_analysis_input(cfg, use_train_input)
      count_dict, *_ = model_stats_fun(model, inputs)
      count = sum(count_dict.values())
      model.train(model_mode)
      return count
  
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 6 for calling a global function with example usage: get_model_stats(model, cfg, mode, use_train_input) and returns: count