

def batch_norm(input, running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-05):
    if training:
        size = list(input.size())
        if (reduce(mul, size[2:], size[0]) == 1):
            raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
    f = torch._C._functions.BatchNorm(running_mean, running_var, training, momentum, eps, torch.backends.cudnn.enabled)
    return f(input, weight, bias)

2f49ef49af027858da9d6b0f4476b2a93097a601
<mask token>

def batch_norm(input, running_mean, running_var, weight=None, bias=None,
    training=False, momentum=0.1, eps=1e-05):
    if training:
        size = list(input.size())
        if reduce(mul, size[2:], size[0]) == 1:
            raise ValueError(
                'Expected more than 1 value per channel when training, got input size {}'
                .format(size))
    f = torch._C._functions.BatchNorm(running_mean, running_var, training,
        momentum, eps, torch.backends.cudnn.enabled)
    return f(input, weight, bias)



def batch_norm(input, running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-05):
    if training:
        size = list(input.size())
        if (reduce(mul, size[2:], size[0]) == 1):
            raise ValueError('Expected more than 1 value per channel when training, got input size {}'.format(size))
    f = torch._C._functions.BatchNorm(running_mean, running_var, training, momentum, eps, torch.backends.cudnn.enabled)
    return f(input, weight, bias)
