请看下面的例子：

batch, sentence_length, embedding_dim = 2, 3, 4
    embedding = torch.randn(batch, sentence_length, embedding_dim)
    print(embedding)
    
# Output:
    tensor([[[-2.1918,  1.2574, -0.3838,  1.3870],
             [-0.4043,  1.2972, -1.7326,  0.4047],
             [ 0.4560,  0.6482,  1.0858,  2.2086]],
    
            [[-1.4964,  0.3722, -0.7766,  0.3062],
             [ 0.9812,  0.1709, -0.9177, -1.2558],
             [-1.1560, -0.0367,  0.5496, -1.1142]]])

应用在嵌入维数上归一化的层范数，我得到：

layer_norm = torch.nn.LayerNorm(embedding_dim)
layer_norm(embedding)

# Output:
tensor([[[-1.5194,  0.8530, -0.2758,  0.9422],
         [-0.2653,  1.2620, -1.4576,  0.4609],
         [-0.9470, -0.6641, -0.0204,  1.6315]],

        [[-1.4058,  0.9872, -0.4840,  0.9026],
         [ 1.3933,  0.4803, -0.7463, -1.1273],
         [-0.9869,  0.5545,  1.3619, -0.9294]]],
       grad_fn=<NativeLayerNormBackward0>)

现在，当我用一个简单的python实现对上述嵌入Tensor的第一个向量进行归一化时，我得到：

a = [-2.1918,  1.2574, -0.3838,  1.3870]
    mean_a = statistics.mean(a)
    var_a = statistics.stdev(a)
    eps = 1e-5
    d = [ ((i-mean_a)/math.sqrt(var_a + eps)) for i in a]
    print(d)
    
    #Output:
[-1.7048934056508998,0.9571791768620398,-0.3094894774404756,1.0572037062293356]

标准化的值和我从PyTorch的Layernorm得到的不一样。我计算Layernorm的方法有什么问题吗？