code: def batch_norm(
      self,
      inputs,
      weight=None,
      bias=None,
      running_mean=None,
      running_var=None,
      training=True,
      eps=1e-5,
      momentum=0.1,
  ):
      running_mean = torch.zeros(
          np.prod(np.array(inputs.data.size()[1]))
      )
      running_var = torch.ones(np.prod(np.array(inputs.data.size()[1])))
      return F.batch_norm(
          inputs,
          running_mean,
          running_var,
          weight,
          bias,
          training,
          momentum,
          eps,
      )
  
imports: import unittest
  import copy
  from collections import OrderedDict
  import numpy
  import torch
  from torch import nn, optim
  from torch.nn import functional
  import higher
label_desc: Use image node_id 2 for calling the _ReferenceNet obj's underlying member method code with example usage: obj.batch_norm(inputs, weight, bias, running_mean, running_var, training, eps, momentum) and returns: F