code: def _batch_norm_flops_compute(
      input,
      running_mean,
      running_var,
      weight=None,
      bias=None,
      training=False,
      momentum=0.1,
      eps=1e-05,
  ):
      has_affine = weight is not None
      if training:
          # estimation
          return input.numel() * (5 if has_affine else 4), 0
      flops = input.numel() * (2 if has_affine else 1)
      return flops, 0
  
imports: import time
  import torch
  import torch.nn
  import torch.nn.functional
  from functools import partial
  from typing import List, Optional
  from collections import OrderedDict
  import numpy
label_desc: Use image node_id 13 for calling a global function with example usage: _batch_norm_flops_compute(input, running_mean, running_var, weight, bias, training, momentum, eps) and returns: flops,  int, unknown,  int