def METHOD_NAME(test_case):
    device = random_device()
    channel = random(1, 4).to(int)
    x = random_tensor(ndim=5, dim1=channel, requires_grad=True).to(device)
    running_mean = random_tensor(ndim=1, dim0=channel, requires_grad=False)
    running_var = random_tensor(ndim=1, dim0=channel, low=0.0, requires_grad=False)
    weight = random_tensor(ndim=1, dim0=channel)
    bias = random_tensor(ndim=1, dim0=channel)
    result = torch.nn.functional.batch_norm(
        input=x,
        running_mean=running_mean,
        running_var=running_var,
        weight=weight,
        bias=bias,
        training=random_bool(),
    )
    return result