Nope.

  
      github.com
  
  
    pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py#L12 30

from .module import Module
from torch.nn.parameter import Parameter
from .. import functional as F




# TODO: check contiguous in THNN
# TODO: use separate backend functions?
class _BatchNorm(Module):


def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
             track_running_stats=True):
    super(_BatchNorm, self).__init__()
    self.num_features = num_features
    self.eps = eps
    self.momentum = momentum
    self.affine = affine
    self.track_running_stats = track_running_stats
    if self.affine:
        self.weight = Parameter(torch.Tensor(num_features))
        self.bias = Parameter(torch.Tensor(num_features))
    else: