Project Name: Lightning-AI+lit-gpt
Language: Python
Statement Type: Constant Assignment

Source Code:
def num_parameters(module: nn.Module, requires_grad: Optional[bool] = None) -> int:
    total = 0
    for p in module.parameters():
        if requires_grad is None or p.requires_grad == requires_grad:
            if hasattr(p, "quant_state"):
                # bitsandbytes 4bit layer support
                total += math.prod(p.quant_state[1])
            else:
                total += p.numel()
    return total

num_parameters(module=GPT(  (lm_head): Linear(in_features=8, out_features=8, bias=False)  (transformer): ModuleDict(    (wte): Embedding(8, 8)    (h): ModuleList(      (0-1): 2 x Block(        (norm_1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)        (attn): CausalSelfAttention(          (attn): Linear(in_features=8, out_features=24, bias=True)          (proj): Linear(in_features=8, out_features=8, bias=True)          (adapter_wte): Embedding(10, 8)        )        (norm_2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)        (mlp): GptNeoxMLP(          (fc): Linear(in_features=8, out_features=32, bias=True)          (proj): Linear(in_features=32, out_features=8, bias=True)        )      )    )    (ln_f): LayerNorm((8,), eps=1e-05, elementwise_affine=True)  )), requires_grad=True)

Selected Statement: total = 0

Function Input:
module: GPT(  (lm_head): Linear(in_features=8, out_features=8, bias=False)  (transformer): ModuleDict(    (wte): Embedding(8, 8)    (h): ModuleList(      (0-1): 2 x Block(        (norm_1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)        (attn): CausalSelfAttention(          (attn): Linear(in_features=8, out_features=24, bias=True)          (proj): Linear(in_features=8, out_features=8, bias=True)          (adapter_wte): Embedding(10, 8)        )        (norm_2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)        (mlp): GptNeoxMLP(          (fc): Linear(in_features=8, out_features=32, bias=True)          (proj): Linear(in_features=32, out_features=8, bias=True)        )      )    )    (ln_f): LayerNorm((8,), eps=1e-05, elementwise_affine=True)  ))
requires_grad: True

Variable Values Before Statement:
Constant: 0

Value After Statement Execution: 0

Variable States During Runtime (line, value):
module: 1
GPT(  (lm_head): Linear(in_features=8, out_features=8, bias=False)  (transformer): ModuleDict(    (wte): Embedding(8, 8)    (h): ModuleList(      (0-1): 2 x Block(        (norm_1): LayerNorm((8,), eps=1e-05, elementwise_affine=True)        (attn): CausalSelfAttention(          (attn): Linear(in_features=8, out_features=24, bias=True)          (proj): Linear(in_features=8, out_features=8, bias=True)          (adapter_wte): Embedding(10, 8)        )        (norm_2): LayerNorm((8,), eps=1e-05, elementwise_affine=True)        (mlp): GptNeoxMLP(          (fc): Linear(in_features=8, out_features=32, bias=True)          (proj): Linear(in_features=32, out_features=8, bias=True)        )      )    )    (ln_f): LayerNorm((8,), eps=1e-05, elementwise_affine=True)  ))
requires_grad: 1
True
total: 2
0
9
4
9
84
9
88
9
168
p: 3
Parameter containing:tensor([[ 2.5051e-01,  7.4097e-02,  2.7177e-01, -5.9998e-02,  8.6356e-02,         -1.3355e-01,  1.0265e-01, -2.0430e-01],        [ 1.4669e-01,  7.2689e-03, -3.2900e-01,  1.0448e-01,  3.4156e-01,          2.7166e-01,  2.9204e-04,  2.8477e-01],        [ 1.3237e-01, -3.6001e-02, -1.9379e-01, -3.1665e-01, -1.9673e-02,          1.4157e-01, -1.6257e-01, -5.8137e-02],        [-3.2136e-01,  2.8897e-01, -1.4786e-01, -9.7668e-02, -9.4666e-02,          7.1392e-02,  3.3215e-02,  6.9191e-02],        [-7.4534e-02,  9.6344e-02, -1.4053e-01,  2.9236e-01, -2.3084e-01,          4.0563e-02, -1.0258e-01,  5.0443e-02],        [ 2.3882e-02, -2.4875e-01,  1.6451e-01,  2.4647e-01, -1.9841e-01,         -2.1787e-01,  4.3476e-02,  1.9028e-01],        [ 2.8256e-01, -3.3064e-01, -2.7154e-01, -3.1312e-02,  1.0199e-01,          2.0320e-01,  2.8572e-01, -3.3013e-01],        [-2.5533e-01,  3.0206e-01,  2.6309e-01, -1.9970e-01, -8.5685e-02,         -3.3387e-01, -1.4620e-01,  3.2047e-01]], device='cuda:0')
3
Parameter containing:tensor([[-0.1873, -1.6913,  1.5322, -0.2604,  0.4374, -0.5390,  0.9018, -1.3492],        [-1.4882, -0.2612,  0.2189, -2.1828,  0.2255,  0.0258,  0.5657, -0.1797],        [-0.3868,  1.8613,  1.2576,  1.1665, -1.1363,  1.3796,  0.1255, -0.7521],        [ 0.6191, -0.9324,  0.2700, -0.7107, -1.0066,  1.0416, -3.4774, -0.1996],        [-1.7366, -0.0246,  0.6478, -1.6823,  0.1165, -0.1131,  0.4057,  1.6760],        [-0.7652, -0.2967, -1.4861, -1.5751,  0.7827, -1.4315,  1.1021,  0.5113],        [ 0.7950, -1.5534, -0.1290,  0.8270, -0.6565, -1.5686, -0.7948, -0.1648],        [-0.6128, -0.9582, -1.6231, -0.1747, -0.1636, -0.4679, -0.8446,  1.3419]],       device='cuda:0')
3
Parameter containing:tensor([1., 1., 1., 1., 1., 1., 1., 1.], device='cuda:0')
3
Parameter containing:tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0')
3
Parameter containing:tensor([[[[0.],          [0.],          [0.],          [0.]]]], device='cuda:0', requires_grad=True)
3
Parameter containing:tensor([[ 0.0889, -0.3279,  0.2429,  0.3349, -0.2547,  0.1056,  0.0371,  0.0550],        [-0.3137,  0.1619,  0.0387,  0.0756, -0.0591, -0.1266,  0.0611,  0.3441],        [-0.1166,  0.2823, -0.3208,  0.0186, -0.1165, -0.2267, -0.0338,  0.2221],        [-0.3125,  0.1147,  0.2699, -0.2272, -0.0160, -0.1895,  0.0019, -0.0198],        [-0.2552,  0.1120, -0.0457,  0.0870, -0.1699, -0.1785, -0.3484,  0.0587],        [-0.0898, -0.0517,  0.3024,  0.1719,  0.3463, -0.2257, -0.0244,  0.1555],        [-0.1015, -0.0824,  0.3325,  0.2130, -0.0858, -0.0207, -0.2525, -0.0335],        [ 0.3278, -0.3148, -0.2676,  0.0028,  0.1150, -0.1644, -0.0803,  0.0623],        [-0.1135,  0.0017,  0.2326, -0.3266,  0.1867, -0.1497, -0.1363,  0.1261],        [-0.2058, -0.1160,  0.3138,  0.3512, -0.1568,  0.0579,  0.0548, -0.1624],        [ 0.3081, -0.2280, -0.0806,  0.2820,  0.0546, -0.2091, -0.1894,  0.2444],        [ 0.0360,  0.2564,  0.1636,  0.0356, -0.0698, -0.1037,  0.0326, -0.2676],        [ 0.1189, -0.0244, -0.2023,  0.2153, -0.2056, -0.1485, -0.3513, -0.1187],        [-0.2108, -0.0278,  0.3477, -0.1904, -0.0801,  0.0428, -0.3516, -0.3473],        [ 0.1788,  0.0423,  0.0529, -0.1381, -0.3178, -0.1098,  0.3002,  0.1866],        [-0.2771,  0.2207, -0.0026,  0.2869, -0.1001,  0.1216, -0.3239, -0.2597],        [-0.1869,  0.0867, -0.2462,  0.1342,  0.2113,  0.0029, -0.0490, -0.0770],        [ 0.3244, -0.1292, -0.1739, -0.1651, -0.1083, -0.0072,  0.1775, -0.3086],        [ 0.1697,  0.2032, -0.0511,  0.2873,  0.0381, -0.2395, -0.1862, -0.1513],        [ 0.1337, -0.1641, -0.0019, -0.2364,  0.1892,  0.2240,  0.0864,  0.0927],        [-0.2440, -0.2606, -0.0284,  0.3234, -0.0603, -0.1891,  0.1467, -0.1607],        [-0.3067,  0.1486,  0.1116,  0.0019, -0.2838,  0.2787, -0.1030, -0.1641],        [ 0.1315,  0.2802, -0.1638, -0.1215,  0.1974,  0.2127,  0.1907,  0.2682],        [-0.2875, -0.2310, -0.0783,  0.1118,  0.3227,  0.0332,  0.1439,  0.1362]],       device='cuda:0')
3
Parameter containing:tensor([-0.0483,  0.1167, -0.1721,  0.0502,  0.0559, -0.1433, -0.1171, -0.2256,         0.0282,  0.2646, -0.1805,  0.3409, -0.2160, -0.1443,  0.0172,  0.3150,        -0.2517,  0.2375, -0.1676,  0.1738, -0.2091, -0.0480, -0.1426, -0.1139],       device='cuda:0')
3
Parameter containing:tensor([[-0.1029, -0.0955, -0.0887,  0.2959,  0.1897,  0.0878, -0.1921, -0.2629],        [-0.2350,  0.0745,  0.0712,  0.1219,  0.3308,  0.0990, -0.3520,  0.0619],        [ 0.2752, -0.3393, -0.0875,  0.1873,  0.0478,  0.0844,  0.2223, -0.3004],        [ 0.0932,  0.1875, -0.2115, -0.1469, -0.0138,  0.3032,  0.0168,  0.0173],        [ 0.2179, -0.2037, -0.1758,  0.2727, -0.0537, -0.3480, -0.2911,  0.1600],        [ 0.0678,  0.0497, -0.1150, -0.1721,  0.2346, -0.3202, -0.1367, -0.2492],        [-0.2869, -0.1534,  0.1284, -0.1534, -0.0133,  0.1076, -0.3000, -0.1015],        [-0.0978, -0.2756,  0.2477, -0.2701,  0.2655, -0.1373,  0.3460, -0.3477]],       device='cuda:0')
3
Parameter containing:tensor([ 0.0784,  0.1758, -0.0545, -0.1572, -0.2092, -0.1891, -0.3255, -0.1430],       device='cuda:0')
3
Parameter containing:tensor([[ 1.7841,  0.7119, -0.2547, -0.1439,  0.3325, -1.1003, -0.8118,  0.7950],        [ 0.8451,  0.6136, -0.4437, -0.5741,  0.8692,  0.7644,  0.3517, -0.5187],        [ 0.1640,  0.7780, -1.4086, -1.6044,  2.2455, -0.3546,  0.6550,  1.1929],        [-0.6591,  0.3522,  0.9287,  0.5068,  0.1667,  0.0170,  0.2055, -0.6080],        [ 0.7926,  1.4860, -0.6619,  0.0146,  1.3542, -1.1758, -0.2057,  0.3901],        [-0.3752, -1.3377, -0.0463,  0.2028, -0.4402,  1.7848, -0.1331, -0.2884],        [-0.3826, -0.1052,  0.4611, -0.9059, -2.0166,  0.2997,  1.2361, -0.4174],        [-2.1430, -0.8734,  0.5893,  0.2488, -0.2177,  0.6726,  1.1039,  1.0060],        [ 0.5812, -0.8599, -0.3468, -0.1275, -0.4766, -0.1236,  0.8605,  1.0199],        [ 0.9441,  0.3637, -1.8332,  0.1539,  0.7132,  0.0950,  0.8609, -0.9934]],       device='cuda:0', requires_grad=True)
3
Parameter containing:tensor([1., 1., 1., 1., 1., 1., 1., 1.], device='cuda:0')
3
Parameter containing:tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0')
3
Parameter containing:tensor([[ 6.5893e-02,  2.1358e-02,  1.4805e-01, -2.8966e-01, -9.9211e-02,         -4.0558e-02, -1.8081e-01, -1.7177e-01],        [ 1.0934e-01, -1.5795e-02, -2.1638e-01, -2.0584e-01, -2.4286e-01,         -1.9081e-01,  2.9576e-01, -1.6553e-01],        [-3.4252e-02,  6.3052e-04,  8.0188e-02, -1.1135e-01, -3.0725e-01,          2.4029e-01,  5.6000e-02,  1.8650e-01],        [-1.5190e-01, -2.3332e-01, -1.5083e-01, -8.7328e-04,  1.0818e-01,          2.6141e-01,  3.3466e-01,  6.6952e-02],        [-9.4581e-02,  2.2194e-02,  6.9640e-02, -1.8661e-01,  1.0973e-01,         -7.1523e-02,  1.0354e-01,  2.3308e-01],        [-3.3942e-01,  3.4300e-01, -3.4288e-01, -9.3225e-02,  3.4878e-01,         -2.8878e-01,  2.5149e-01, -9.0007e-03],        [-1.6085e-01,  3.5229e-01,  1.6255e-01,  3.3887e-02, -7.4003e-02,         -3.3468e-01,  2.3890e-01, -2.3619e-01],        [ 7.8607e-02, -2.8461e-01,  5.0856e-02,  8.1235e-02,  1.0375e-01,         -3.0794e-01, -6.7361e-02,  3.3582e-01],        [ 4.5722e-02, -1.1111e-02, -1.7835e-01,  1.1545e-01,  6.1457e-02,         -9.7693e-02,  1.0451e-02,  9.6269e-02],        [ 1.8339e-01,  4.3673e-02,  4.5492e-03, -1.3804e-01, -3.1340e-01,          2.9526e-01,  2.3653e-01,  2.7255e-01],        [ 8.2121e-02, -1.4972e-01, -2.2425e-01,  3.7039e-02, -2.2301e-01,         -1.5539e-01, -5.0423e-02,  2.3112e-01],        [-7.5777e-02, -2.9300e-01, -2.1856e-01,  6.8585e-02, -3.2383e-01,          2.3423e-02, -2.2967e-01, -6.8076e-02],        [ 4.8495e-02, -2.5661e-01,  2.1968e-01,  3.3006e-01,  3.2420e-01,          3.0799e-01,  6.8646e-03, -1.5422e-02],        [ 2.1354e-01,  2.3493e-01, -1.5334e-01, -3.2478e-01,  1.2354e-01,          1.9622e-01,  1.3425e-01,  4.7188e-02],        [ 2.9492e-01, -6.1003e-02, -3.3010e-01, -2.0462e-01,  1.7078e-04,         -8.6789e-03, -4.8258e-02,  1.4198e-01],        [-6.6475e-02, -1.8329e-01, -1.8995e-02,  1.3849e-01, -6.0820e-03,          2.8869e-01,  2.6644e-01,  8.7311e-03],        [ 2.7676e-01, -8.9596e-02, -1.2223e-01, -6.2885e-02, -3.0729e-01,         -2.0378e-01, -2.6045e-01,  3.4084e-01],        [ 5.7260e-02, -1.7303e-02, -1.3779e-01, -3.2510e-01,  2.3286e-01,         -1.5465e-01,  2.8172e-01,  8.2384e-02],        [ 2.1787e-01, -1.9439e-01,  2.8290e-01, -9.9440e-02,  7.4783e-03,         -1.4567e-02,  1.0209e-02,  2.4470e-01],        [ 5.9790e-02,  2.4915e-02,  2.7909e-01,  1.0035e-02, -9.5062e-02,          1.5389e-01,  1.5549e-01, -7.9868e-02],        [-3.4455e-01,  1.3742e-01, -8.2051e-02, -2.4700e-01,  1.1334e-01,         -2.1086e-02, -3.1369e-01, -3.3946e-01],        [-1.9585e-01, -2.9069e-01,  2.4966e-01, -2.0562e-01,  7.9724e-02,          2.5096e-01, -3.3514e-01, -1.1402e-01],        [-4.5188e-02, -2.8768e-01,  5.2162e-02,  2.8394e-01,  3.3272e-01,         -1.6050e-01, -2.3109e-01, -2.6183e-01],        [-2.7715e-01, -2.8243e-01,  1.6490e-01, -1.6387e-01, -1.5267e-01,         -3.0654e-01, -2.8063e-01, -3.3728e-01],        [ 3.2159e-01, -2.4516e-01, -3.2917e-01,  7.4709e-02, -2.4596e-01,         -2.8767e-01, -1.2852e-01, -2.3774e-01],        [ 3.2401e-01,  2.0746e-01, -1.3909e-01, -2.0924e-01,  1.7289e-01,          3.8450e-02,  7.6429e-02,  2.8703e-01],        [ 1.3821e-01, -1.8876e-01, -2.6919e-01,  4.1932e-02, -8.8940e-02,          7.4909e-02,  2.9936e-01, -2.3437e-01],        [-2.2846e-01, -2.4945e-01,  1.4291e-01, -2.2226e-01,  1.0372e-01,         -1.4193e-01, -1.3718e-01, -1.2027e-01],        [-1.6028e-01,  6.6377e-02, -9.3952e-03, -4.8865e-02, -2.1320e-01,          2.8190e-01, -2.0719e-01, -2.0863e-01],        [-2.7065e-01,  1.8744e-01,  1.2356e-01,  3.2627e-01,  2.4614e-01,          2.2572e-01, -3.4430e-01, -2.1322e-01],        [-1.4362e-01, -2.2222e-01, -8.3469e-02,  2.3390e-02, -9.5809e-02,         -2.0266e-01,  9.1512e-02, -3.3260e-01],        [-2.3665e-01, -8.1854e-02, -7.1756e-02, -2.0481e-01,  1.4672e-01,         -6.3378e-03,  1.8597e-01,  3.4100e-01]], device='cuda:0')
3
Parameter containing:tensor([-0.0693,  0.2328, -0.3503, -0.0378,  0.1455,  0.0200, -0.2220,  0.2883,        -0.2709,  0.3166,  0.1389,  0.2205, -0.2029, -0.0817, -0.2797,  0.0571,        -0.1136,  0.2654, -0.2372, -0.1622, -0.1799, -0.1930, -0.2183,  0.1699,         0.3248, -0.2964,  0.1801, -0.0725, -0.2218,  0.2408,  0.3513,  0.3329],       device='cuda:0')
3
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Parameter containing:tensor([-0.0323, -0.1023,  0.0092, -0.0543,  0.0628,  0.1554,  0.1386,  0.1144],       device='cuda:0')
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Parameter containing:tensor([1., 1., 1., 1., 1., 1., 1., 1.], device='cuda:0')
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Parameter containing:tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0')
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Parameter containing:tensor([[[[0.],          [0.],          [0.],          [0.]]]], device='cuda:0', requires_grad=True)
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Parameter containing:tensor([ 0.2181,  0.2350, -0.2638,  0.2656, -0.1387,  0.3444,  0.0198, -0.1788],       device='cuda:0')
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Parameter containing:tensor([[ 1.1537,  1.4106,  1.5544, -1.0306, -1.3801,  0.2603, -0.6515,  1.5445],        [-0.1275, -0.6874, -0.6158, -0.8035, -0.0088, -1.2591,  0.3704,  1.4032],        [-1.0058,  0.0532,  0.4710,  1.2358,  0.1716, -0.8462,  0.8084, -1.2172],        [ 1.7788,  0.7106, -1.3180, -2.5337, -0.5739, -1.1140,  1.0843,  0.3369],        [ 0.3764, -1.4538,  1.3136, -0.0065,  0.3045, -1.1442, -0.2558,  1.2981],        [ 0.0188, -0.3169, -1.2457,  1.2154,  0.2243,  1.8292, -2.1265, -1.7702],        [-0.4025,  0.5508, -1.0771,  0.8800, -0.4297,  0.6837, -1.3463, -0.7835],        [-0.0045,  1.1033, -1.2160, -0.4731,  0.8819,  0.0372,  0.7367,  0.3115],        [-0.5226,  0.3311, -0.0913,  0.7536,  1.3422, -0.2453, -0.9801,  0.5688],        [-0.2757,  1.6502,  0.6107,  1.4373, -0.5155,  0.7005, -1.1838,  0.8372]],       device='cuda:0', requires_grad=True)
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Parameter containing:tensor([1., 1., 1., 1., 1., 1., 1., 1.], device='cuda:0')
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Parameter containing:tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0')
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Parameter containing:tensor([ 0.0230,  0.2693, -0.1822,  0.1215,  0.0237,  0.0995,  0.3511, -0.0494,        -0.1502,  0.1793,  0.2525, -0.0892, -0.2716, -0.1036, -0.1978, -0.0045,        -0.0400,  0.2644,  0.1202, -0.2093, -0.1216, -0.0320, -0.2973,  0.3021,         0.1840, -0.1094,  0.3364, -0.2633,  0.2542,  0.0856,  0.1225,  0.2950],       device='cuda:0')
3
Parameter containing:tensor([[ 3.2091e-02, -1.0682e-01, -1.3673e-01, -6.4852e-02, -1.5851e-01,         -1.4597e-02, -3.4998e-02, -1.7307e-02, -3.1298e-02,  1.3823e-01,          8.6090e-02,  1.6641e-02, -5.0713e-02,  9.0618e-02,  1.2002e-01,          1.0725e-01, -1.2712e-01, -1.4069e-01,  4.4547e-02,  1.4398e-01,          2.2691e-02, -1.6055e-01, -1.0734e-01,  1.2551e-01, -9.9248e-02,         -1.3766e-01,  2.7332e-02,  6.6128e-02,  1.4520e-01, -6.3990e-02,         -8.8924e-02,  1.6574e-01],        [-8.5860e-02,  1.6455e-01,  5.5703e-03, -7.5762e-03, -5.8784e-02,          8.7587e-02,  1.7518e-01, -1.2245e-01, -1.6567e-01,  1.5311e-01,          1.3423e-01,  1.5241e-01,  1.6841e-01,  1.1972e-01, -4.9093e-02,         -4.3115e-02,  4.8810e-02, -1.3533e-01,  4.2802e-02, -9.4440e-02,          6.5721e-02, -1.2723e-01,  2.0516e-02,  1.1529e-01, -6.3554e-02,          3.7811e-02, -7.6571e-02, -4.0653e-02, -3.6779e-03, -1.3432e-01,         -7.0104e-02, -1.6757e-01],        [ 3.1871e-02,  9.5419e-02,  2.4403e-02, -7.4098e-02,  3.5393e-02,         -1.2427e-01,  8.2556e-02, -4.0956e-02,  1.1842e-01, -2.6363e-02,         -1.5294e-01, -1.2758e-01,  4.8718e-02, -1.7604e-01,  7.0715e-02,         -1.4409e-01,  4.9713e-02,  1.0302e-01,  1.6939e-01,  5.8464e-02,         -1.2441e-01, -6.2853e-02,  4.0320e-02, -1.2951e-01, -6.3597e-02,          7.8169e-02, -1.1068e-01, -1.4177e-01,  1.1039e-01, -1.4397e-02,         -1.0798e-01,  1.2992e-01],        [-6.8907e-02,  6.8767e-02,  1.2048e-01, -9.3299e-02, -9.3412e-02,          1.4834e-01,  1.3076e-01,  8.3786e-02,  5.0215e-02,  7.6105e-02,          9.1665e-02, -1.7451e-01,  1.7482e-01,  4.2569e-02, -1.7710e-02,         -5.2226e-02,  4.1657e-03, -8.9350e-02, -1.6638e-01, -3.4854e-02,          1.1221e-01,  5.0713e-02,  1.4936e-01,  9.5436e-02, -1.8535e-02,         -1.5881e-01, -1.4284e-01,  5.2121e-02,  1.5759e-02, -1.0651e-01,         -1.1450e-01, -3.8950e-02],        [ 6.4010e-02,  4.1832e-02, -1.7165e-02,  1.5794e-02, -1.3269e-01,          7.4074e-02, -1.3031e-01,  8.5265e-02, -6.7803e-02, -1.9770e-03,         -5.1846e-02, -1.1780e-01,  7.4514e-04,  4.0716e-02, -1.4831e-01,          1.6150e-01,  1.7425e-01,  7.8152e-02,  1.5945e-01,  2.6147e-02,         -2.3951e-02,  1.2115e-01,  3.7625e-02, -6.1239e-02, -1.2143e-01,         -1.0947e-01,  7.2787e-02,  1.4134e-01,  7.7765e-02,  1.1258e-01,         -3.1872e-02,  2.5150e-02],        [-7.7370e-02,  1.5542e-01, -1.4555e-01, -1.2390e-01, -1.1911e-01,         -2.1942e-02,  1.5837e-01, -1.5696e-01,  5.3065e-02, -3.7610e-02,          1.2573e-02,  3.9276e-02, -1.0505e-01, -1.0437e-01, -1.3930e-01,          9.3889e-02, -5.2252e-02, -1.0276e-02, -9.2492e-02,  1.0355e-01,         -1.5270e-01,  1.1641e-01, -5.1292e-02, -5.1706e-02, -1.7213e-01,          1.2448e-01,  4.8178e-03,  6.7001e-02, -1.3091e-01, -1.3727e-01,          1.0331e-01, -5.4297e-02],        [-9.6762e-02, -8.1677e-02, -1.3128e-01,  7.4091e-02, -1.2999e-01,          5.5845e-05,  6.3019e-02,  1.5693e-01,  2.6206e-02,  1.4452e-01,          1.5972e-01, -1.1917e-01,  1.7290e-01,  1.6383e-01, -1.6653e-01,          1.5148e-01, -5.6980e-02, -2.2292e-02, -9.6283e-03, -7.7248e-02,          6.7019e-02, -9.5227e-02, -1.3011e-01,  1.2579e-01,  2.6667e-02,          7.3271e-02,  2.2565e-02,  1.1339e-01,  7.8180e-02,  1.0898e-01,          7.3876e-02,  1.3928e-01],        [ 4.6086e-02, -1.7529e-01, -1.6908e-01, -4.2175e-02,  1.4678e-01,         -4.8583e-02,  2.5431e-02, -9.0840e-02,  6.5542e-02, -1.2747e-01,          1.0576e-01, -1.2599e-01,  1.0223e-02, -1.5799e-01,  9.4894e-02,         -1.7279e-01,  1.7128e-01,  7.1158e-02, -1.6636e-01,  3.0011e-02,         -3.8756e-02, -6.6649e-02, -1.0032e-01, -9.6605e-02, -1.0353e-01,         -4.9014e-02,  3.7320e-02, -6.4673e-02, -1.6552e-01,  1.3066e-01,         -2.5382e-02, -4.8320e-02]], device='cuda:0')
3
Parameter containing:tensor([-0.0858, -0.1672, -0.0081, -0.1258,  0.1006,  0.0445,  0.0299,  0.0104],       device='cuda:0')
3
Parameter containing:tensor([1., 1., 1., 1., 1., 1., 1., 1.], device='cuda:0')
3
Parameter containing:tensor([0., 0., 0., 0., 0., 0., 0., 0.], device='cuda:0')