code: def params_count(model, ignore_bn=False):
      """
      Compute the number of parameters.
      Args:
          model (model): model to count the number of parameters.
      """
      if not ignore_bn:
          return np.sum([p.numel() for p in model.parameters()]).item()
      else:
          count = 0
          for m in model.modules():
              if not isinstance(m, nn.BatchNorm3d):
                  for p in m.parameters(recurse=False):
                      count += p.numel()
      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 2 for calling a global function with example usage: params_count(model, ignore_bn) and returns: count, np