I am trying to run inference on a jit traced model in c++ and currently the output I get in Python is different than the output I get in C++.
At first it seems like there is something wrong with the jit in the C++, while this still could be the case, I’m not sure any more as I spotted some small deviations in the input tensor in C++ as well. I believe I did everything as instructed by the documentation so that might as well show an issue in torch::from_blob.
Therefore in order to make sure which is the case, Here are the snippets both in Python and C++ plus the sample input and sample model to test it yourself.
I also attached the weights and input comparisions in addition to the output I get from each of  them so you can better see the difference and hopefully tell me what is wrong here :

download the jit models : models_jit.zip and extract them somewhere.
get the sample image as well :



for Pytorch run the following snippet of code :
import cv2
import torch
from PIL import Image 
import math

pnet_jit = torch.jit.load('D:/Codes/MTCNN/pnet.jit')
img = Image.open('D:/Codes/imgs/profile6.jpg')
width, height = img.size
scale = 0.6
sw, sh = math.ceil(width * scale), math.ceil(height * scale)
img = img.resize((sw, sh), Image.BILINEAR)
img = np.asarray(img, 'float32')

# preprocess it 
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, 0)
img = (img - 127.5) * 0.0078125
img = torch.from_numpy(img)
pnet_jit.eval()

with torch.no_grad():
    output = pnet_jit(img)
    offsets = output[0].data.cpu().numpy()
    probs = output[1].data.cpu().numpy()[0, 1, :, :]
    np.set_printoptions(precision=6, suppress=True)

    print(f'offsets.shape: {offsets.shape}')
    print(f'offsets[:, :, :3, :3]: {offsets[:,:,:3,:3]}')
    print(f'probs.shape: {probs.shape}')
    print(f'probs[:3,:3]: {probs[:3,:3]}')

# output: 
"""
offsets.shape: (1, 4, 46, 85)
offsets[:, :, :3, :3]: [[
  [[-0.046459 -0.044137 -0.042813]
   [-0.018772 -0.012278  0.003612]
   [-0.00507  -0.001392  0.008206]]

  [[-0.020479 -0.023341 -0.01451 ]
   [ 0.009624  0.010831  0.011209]
   [-0.029733 -0.035114 -0.043128]]

  [[-0.018409 -0.002512  0.006032]
   [ 0.007651  0.017719  0.030373]
   [ 0.032841  0.039231  0.046088]]

  [[ 0.212606  0.226369  0.236699]
   [ 0.239645  0.243053  0.239074]
   [ 0.215258  0.212819  0.203118]]]]
probs.shape: (46, 85)
probs[:3,:3]: [
 [0.0002   0.000134 0.000091]
 [0.000558 0.000553 0.000529]
 [0.001526 0.001293 0.001365]]
"""

for C++:
#include <iostream>
#include <torch/torch.h>
#include <torch/script.h>
using namespace torch::indexing;

#include <opencv2/core.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/highgui/highgui.hpp>

void test15()
{
    std::string pnet_path = "D:/Codes//MTCNN/pnet.jit"; 
    cv::Mat img = cv::imread("D:/Codes/imgs/profile6.jpg");
    auto pnet = torch::jit::load(pnet_path);
    pnet.to(c10::DeviceType::CPU);
    pnet.eval();

    torch::NoGradGuard nograd;

    int width = img.cols;
    int height = img.rows;
    float scale = 0.6f;
    int sw = int(std::ceil(width * scale));
    int sh = int(std::ceil(height * scale));

    //cv::Mat img;
    cv::resize(img, img, cv::Size(sw, sh), 0, 0, 1);

    auto tensor_image = torch::from_blob(img.data, { img.rows, img.cols, img.channels() }, at::kByte);
    tensor_image = tensor_image.permute({ 2,0,1 });
    tensor_image.unsqueeze_(0);
    tensor_image = tensor_image.toType(c10::kFloat).sub(127.5).mul(0.0078125);
    tensor_image.to(c10::DeviceType::CPU);

    auto outputs = pnet.forward({ tensor_image }).toTuple();
    auto output0 = outputs->elements()[0].toTensor();
    auto output1 = outputs->elements()[1].toTensor();

    auto probs = output1.data().cpu().index({ 0, 1, Slice(), Slice() });
    auto offsets = output0.data().cpu();

    std::cout << "offsets.shape: " << offsets.sizes() << std::endl;
    std::cout << "offsets: " << offsets.index({ Slice(), Slice(), Slice(None,5), Slice(None,5) }) << std::endl;
    std::cout << "probs.shape: " << probs.sizes() << std::endl;
    std::cout << "probs: " << probs.index({ Slice(None,5), Slice(None,5) })<< std::endl;

}

/*output:
offsets.shape: [1, 4, 46, 85]
offsets: (1,1,.,.) =
 0.01 *
  0.1006  1.2322  1.4785  0.8394 -0.9099
  -2.9587 -2.2280  0.9300 -0.2308 -1.8565
  -3.9297 -2.1750 -0.1311 -0.4570 -1.6974
  -3.7373 -2.4041 -1.1337 -1.4220 -1.6173
  -2.1611 -1.3214 -1.1488 -1.6253 -1.8294

(1,2,.,.) =
 0.01 *
  1.3772  1.3971  1.0763  0.8742 -0.0868
  -1.2813 -0.8563 -0.8726 -1.3861 -3.3406
  -7.3669 -7.6641 -7.8015 -9.1806 -9.6936
  -12.2440 -13.5946 -14.4936 -14.9172 -14.9992
  -15.9835 -17.3126 -18.1479 -18.4232 -20.1504

(1,3,.,.) =
  0.0624  0.0926  0.1035  0.0817  0.0505
  0.0357  0.0547  0.0693  0.0535  0.0191
  0.0079  0.0276  0.0339  0.0251  0.0062
 -0.0169 -0.0048  0.0072  0.0056  0.0053
  0.0048  0.0221  0.0233  0.0170  0.0237

(1,4,.,.) =
  0.2901  0.2963  0.2959  0.2804  0.2496
  0.2618  0.2771  0.2569  0.2516  0.2052
  0.1862  0.1827  0.1650  0.1467  0.1389
  0.1131  0.0967  0.0869  0.0875  0.0919
  0.1016  0.0981  0.0872  0.0815  0.0741
[ CPUFloatType{1,4,5,5} ]
probs.shape: [46, 85]
probs: 0.001 *
 0.1459  0.1035  0.1115  0.1166  0.1958
  0.6678  0.4910  0.8298  0.9519  2.0390
  1.5165  1.6454  2.4029  3.1594  3.8440
  1.4742  2.1590  2.5001  3.3515  3.9793
  0.3751  0.3293  0.3784  0.4359  0.4375
[ CPUFloatType{5,5} ]

*/

And Here are the information regarding Weights and Their input (to make sure they are identical):
Weight comparison:
for key, module in pnet_jit.named_modules():
    print(f'module name: {key}: ')   
    if 'features.conv1' in key:
        for name, param in module.named_parameters():
            print(f'{name}: {param}')


Pytorch output
weight: tensor([[[[-0.0816,  0.8359,  1.2465],
          [-0.0150,  0.2380, -1.3453],
          [ 0.1246, -0.4108, -0.5808]],

         [[-0.2663,  0.8522,  0.5174],
          [ 0.2975,  0.3202, -1.0906],
          [ 0.1030, -0.3321, -0.4963]],

         [[-0.1369,  0.0164,  0.0337],
          [ 0.1016,  0.4359, -0.2164],
          [-0.1793, -0.1034,  0.0597]]],


        [[[ 0.4879, -0.8749, -2.4024],
          [ 0.3251, -0.7900, -0.7244],
          [ 0.7851,  0.2713,  0.2545]],

         [[-0.4946,  0.2710,  1.1370],
          [-0.1313, -0.3163,  0.5235],
          [-0.2501, -0.1840,  0.6914]],

         [[-0.2958,  1.1880,  1.2591],
          [ 0.2304,  0.4256,  0.6742],
          [-0.6014, -0.2309, -0.9644]]],


        [[[-0.7042,  0.1816, -0.3616],
          [-0.0549,  0.1669,  0.2031],
          [-0.5338, -0.0997, -0.2377]],

         [[ 0.6441,  0.2686,  0.3320],
          [ 0.5240,  0.1196,  0.3597],
          [ 0.5634,  0.0950,  0.3147]],

         [[ 0.3934, -0.2291,  0.3150],
          [-0.1116, -0.4287, -0.3120],
          [ 0.7103, -0.1339,  0.1115]]],


        [[[-2.0421, -0.5308,  0.1612],
          [-1.3518,  0.9715,  0.6593],
          [ 0.2722,  1.1669,  0.4001]],

         [[-0.0620, -0.1261,  0.5850],
          [-0.3468,  0.0133,  0.5690],
          [-0.5115,  0.1502, -0.2370]],

         [[ 0.1861, -0.4167, -0.2363],
          [-0.3585, -0.3084,  0.3250],
          [-0.0993,  0.1994,  0.8541]]],


        [[[-1.4441,  3.1158, -1.2826],
          [-0.8900,  0.8446, -0.7795],
          [ 0.2078,  0.2566, -0.0483]],

         [[-1.1270,  2.3380, -1.1374],
          [-0.0492,  0.3376, -0.4688],
          [ 0.0759,  0.0539, -0.0208]],

         [[-0.5917,  0.9548, -0.5569],
          [ 0.4371, -0.5969,  0.3891],
          [-0.0968, -0.1839,  0.2156]]],


        [[[-0.3098,  1.1087,  0.2215],
          [-1.2481,  0.5376,  0.7132],
          [-0.6857, -0.4005,  0.4775]],

         [[-0.3679,  0.5855,  0.1316],
          [-1.0780,  0.2779,  0.6630],
          [-0.6459, -0.7225,  0.3408]],

         [[-0.1987,  0.2181,  0.4195],
          [-0.5669,  0.0587,  0.2915],
          [ 0.3173, -0.4066,  0.2406]]],


        [[[-0.5409, -0.6875, -1.7550],
          [ 0.1231,  0.3844, -0.8736],
          [ 0.7980,  1.7604,  1.1732]],

         [[ 0.2150, -0.1214,  0.0584],
          [ 0.4521,  0.3732,  0.2367],
          [-0.9273, -0.4391, -0.2047]],

         [[-0.2877, -0.3038, -0.0793],
          [-0.0045,  0.1454, -0.2187],
          [ 0.4220,  0.4764, -0.2135]]],


        [[[ 1.0726,  0.6265, -1.7609],
          [ 0.9449,  1.0202, -1.6218],
          [ 0.8421,  0.5825, -1.6865]],

         [[-0.2256,  0.4871, -0.2873],
          [-0.1027,  0.5822, -0.3538],
          [-0.3428,  0.2516,  0.0826]],

         [[ 0.4017, -0.1654, -0.4704],
          [ 0.2889,  0.3179, -0.6670],
          [ 0.2409,  0.1736, -0.2978]]],


        [[[-0.9578, -1.2807, -0.7732],
          [ 1.9559,  2.3139,  1.2954],
          [-0.9624, -1.0256, -0.5687]],

         [[-0.1683, -0.8830, -0.1274],
          [ 0.7475,  1.3671,  0.5782],
          [-0.5269, -0.6681, -0.3480]],

         [[-0.3523, -0.3842, -0.2158],
          [ 0.5751,  0.6146,  0.2712],
          [-0.1618, -0.2791, -0.0442]]],


        [[[-0.5648, -1.5434,  0.6763],
          [-0.2447, -0.5838,  0.2134],
          [ 0.0059, -0.2488,  0.3020]],

         [[ 2.3330,  0.8325,  0.5582],
          [ 0.9319,  0.9614, -0.1041],
          [ 0.1203, -0.2600, -0.6534]],

         [[-1.3353,  0.2698, -1.0193],
          [-0.9542,  0.3225, -0.4546],
          [-0.0500,  0.3450,  0.5087]]]], requires_grad=True)
bias: tensor([-0.0828,  0.9198,  1.1887,  0.0868, -0.0194, -0.0315,  0.1324, -0.0522,
         0.0077,  0.6673], requires_grad=True)
module name: features.prelu1: 
module name: features.pool1: 
module name: features.conv2: 
module name: features.prelu2: 
module name: features.conv3: 
module name: features.prelu3: 
module name: conv4_1: 
module name: conv4_2: 


cpp:
for (auto m: pnet.named_modules()) {
    std::cout << m.name << std::endl;
    if (m.name == "features.conv1") {
        for (auto p : m.value.named_parameters()) {
            std::cout << p.name << ": " << p.value << std::endl;
        }}}


 libtorch output
features.conv1
weight: (1,1,.,.) =
 -0.0816  0.8359  1.2465
 -0.0150  0.2380 -1.3453
  0.1246 -0.4108 -0.5808

(2,1,.,.) =
  0.4879 -0.8749 -2.4024
  0.3251 -0.7900 -0.7244
  0.7851  0.2713  0.2545

(3,1,.,.) =
 -0.7042  0.1816 -0.3616
 -0.0549  0.1669  0.2031
 -0.5338 -0.0997 -0.2377

(4,1,.,.) =
 -2.0421 -0.5308  0.1612
 -1.3518  0.9715  0.6593
  0.2722  1.1669  0.4001

(5,1,.,.) =
 -1.4441  3.1158 -1.2826
 -0.8900  0.8446 -0.7795
  0.2078  0.2566 -0.0483

(6,1,.,.) =
 -0.3098  1.1087  0.2215
 -1.2481  0.5376  0.7132
 -0.6857 -0.4005  0.4775

(7,1,.,.) =
 -0.5409 -0.6875 -1.7550
  0.1231  0.3844 -0.8736
  0.7980  1.7604  1.1732

(8,1,.,.) =
  1.0726  0.6265 -1.7609
  0.9449  1.0202 -1.6218
  0.8421  0.5825 -1.6865

(9,1,.,.) =
 -0.9578 -1.2807 -0.7732
  1.9559  2.3139  1.2954
 -0.9624 -1.0256 -0.5687

(10,1,.,.) =
 -0.5648 -1.5434  0.6763
 -0.2447 -0.5838  0.2134
  0.0059 -0.2488  0.3020

(1,2,.,.) =
 -0.2663  0.8522  0.5174
  0.2975  0.3202 -1.0906
  0.1030 -0.3321 -0.4963

(2,2,.,.) =
 -0.4946  0.2710  1.1370
 -0.1313 -0.3163  0.5235
 -0.2501 -0.1840  0.6914

(3,2,.,.) =
  0.6441  0.2686  0.3320
  0.5240  0.1196  0.3597
  0.5634  0.0950  0.3147

(4,2,.,.) =
 -0.0620 -0.1261  0.5850
 -0.3468  0.0133  0.5690
 -0.5115  0.1502 -0.2370

(5,2,.,.) =
 -1.1270  2.3380 -1.1374
 -0.0492  0.3376 -0.4688
  0.0759  0.0539 -0.0208

(6,2,.,.) =
 -0.3679  0.5855  0.1316
 -1.0780  0.2779  0.6630
 -0.6459 -0.7225  0.3408

(7,2,.,.) =
  0.2150 -0.1214  0.0584
  0.4521  0.3732  0.2367
 -0.9273 -0.4391 -0.2047

(8,2,.,.) =
 -0.2256  0.4871 -0.2873
 -0.1027  0.5822 -0.3538
 -0.3428  0.2516  0.0826

(9,2,.,.) =
 -0.1683 -0.8830 -0.1274
  0.7475  1.3671  0.5782
 -0.5269 -0.6681 -0.3480

(10,2,.,.) =
  2.3330  0.8325  0.5582
  0.9319  0.9614 -0.1041
  0.1203 -0.2600 -0.6534

(1,3,.,.) =
 -0.1369  0.0164  0.0337
  0.1016  0.4359 -0.2164
 -0.1793 -0.1034  0.0597

(2,3,.,.) =
 -0.2958  1.1880  1.2591
  0.2304  0.4256  0.6742
 -0.6014 -0.2309 -0.9644

(3,3,.,.) =
  0.3934 -0.2291  0.3150
 -0.1116 -0.4287 -0.3120
  0.7103 -0.1339  0.1115

(4,3,.,.) =
  0.1861 -0.4167 -0.2363
 -0.3585 -0.3084  0.3250
 -0.0993  0.1994  0.8541

(5,3,.,.) =
 -0.5917  0.9548 -0.5569
  0.4371 -0.5969  0.3891
 -0.0968 -0.1839  0.2156

(6,3,.,.) =
 -0.1987  0.2181  0.4195
 -0.5669  0.0587  0.2915
  0.3173 -0.4066  0.2406

(7,3,.,.) =
 -0.2877 -0.3038 -0.0793
 -0.0045  0.1454 -0.2187
  0.4220  0.4764 -0.2135

(8,3,.,.) =
  0.4017 -0.1654 -0.4704
  0.2889  0.3179 -0.6670
  0.2409  0.1736 -0.2978

(9,3,.,.) =
 -0.3523 -0.3842 -0.2158
  0.5751  0.6146  0.2712
 -0.1618 -0.2791 -0.0442

(10,3,.,.) =
 -1.3353  0.2698 -1.0193
 -0.9542  0.3225 -0.4546
 -0.0500  0.3450  0.5087
[ CPUFloatType{10,3,3,3} ]
bias: -0.0828
 0.9198
 1.1887
 0.0868
-0.0194
-0.0315
 0.1324
-0.0522
 0.0077
 0.6673
[ CPUFloatType{10} ]
features.prelu1
features.pool1
features.conv2
features.prelu2
features.conv3
features.prelu3
conv4_1
conv4_2


Input comparison :
and here are the tensor values both in Python and C++
Pytorch input (img[:, :, :10, :10]):

img: tensor([[
    [[0.3555,  0.3555,  0.3477,  0.3555,  0.3711,  0.3945,  0.3945,  0.3867,  0.3789,  0.3789],
    [ 0.3477,  0.3555,  0.3555,  0.3555,  0.3555,  0.3555,  0.3555,  0.3477,  0.3398,  0.3398],
    [ 0.3320,  0.3242,  0.3320,  0.3242,  0.3320,  0.3398,  0.3398,  0.3242,  0.3164,  0.3242],
    [ 0.2852,  0.2930,  0.2852,  0.2852,  0.2930,  0.2930,  0.2930,  0.2852,  0.2773,  0.2773],
    [ 0.2539,  0.2617,  0.2539,  0.2617,  0.2539,  0.2148,  0.2148,  0.2148,  0.2070,  0.2070],
    [ 0.1914,  0.1914,  0.1836,  0.1836,  0.1758,  0.1523,  0.1367,  0.1211,  0.0977,  0.0898],
    [ 0.1367,  0.1211,  0.0977,  0.0820,  0.0742,  0.0586,  0.0273,  -0.0195, -0.0742, -0.0820],
    [-0.0039, -0.0273, -0.0508, -0.0664, -0.0898, -0.1211, -0.1367, -0.1523, -0.1758, -0.1758],
    [-0.2070, -0.2070, -0.2148, -0.2227, -0.2148, -0.1992, -0.1992, -0.1836, -0.1680, -0.1680],
    [-0.2539, -0.2461, -0.2383, -0.2305, -0.2227, -0.1914, -0.1836, -0.1758, -0.1680, -0.1602]],

    [[0.8398,  0.8398,  0.8320,  0.8242,  0.8320,  0.8477,  0.8398, 0.8320,  0.8164,  0.8164],
    [ 0.8320,  0.8242,  0.8164,  0.8164,  0.8086,  0.8008,  0.7930, 0.7852,  0.7695,  0.7695],
    [ 0.7852,  0.7852,  0.7773,  0.7695,  0.7695,  0.7617,  0.7539, 0.7383,  0.7305,  0.7148],
    [ 0.7227,  0.7070,  0.7070,  0.6992,  0.6914,  0.6836,  0.6836, 0.6680,  0.6523,  0.6367],
    [ 0.6289,  0.6211,  0.6211,  0.6211,  0.6055,  0.5586,  0.5508, 0.5352,  0.5273,  0.5039],
    [ 0.4805,  0.4727,  0.4648,  0.4648,  0.4570,  0.4180,  0.3945, 0.3633,  0.3477,  0.3164],
    [ 0.3555,  0.3398,  0.3086,  0.2930,  0.2695,  0.2461,  0.2070, 0.1523,  0.1055,  0.0820],
    [ 0.1367,  0.1133,  0.0820,  0.0508,  0.0273, -0.0117, -0.0352, -0.0508, -0.0820, -0.0898],
    [-0.1211, -0.1289, -0.1445, -0.1602, -0.1602, -0.1523, -0.1523, -0.1367, -0.1367, -0.1289],
    [-0.2070, -0.1992, -0.1992, -0.1992, -0.1992, -0.1680, -0.1680, -0.1602, -0.1523, -0.1445]],

    [[0.9492,  0.9414,  0.9336,  0.9180,  0.9180,  0.9336,  0.9258, 0.9023,  0.8867,  0.9023],
    [ 0.9258,  0.9258,  0.9102,  0.9023,  0.8945,  0.8789,  0.8633, 0.8477,  0.8320,  0.8398],
    [ 0.8711,  0.8633,  0.8555,  0.8477,  0.8320,  0.8242,  0.8086, 0.7930,  0.7852,  0.7773],
    [ 0.7852,  0.7773,  0.7617,  0.7539,  0.7461,  0.7305,  0.7148, 0.6992,  0.6914,  0.6836],
    [ 0.6758,  0.6680,  0.6602,  0.6602,  0.6367,  0.5820,  0.5742, 0.5508,  0.5430,  0.5273],
    [ 0.5117,  0.5117,  0.4961,  0.4883,  0.4727,  0.4336,  0.4102, 0.3711,  0.3477,  0.3242],
    [ 0.3867,  0.3711,  0.3398,  0.3164,  0.2930,  0.2539,  0.2148, 0.1523,  0.1055,  0.0820],
    [ 0.1680,  0.1445,  0.1055,  0.0742,  0.0352, -0.0039, -0.0273, -0.0586, -0.0820, -0.0898],
    [-0.0898, -0.0977, -0.1211, -0.1367, -0.1445, -0.1445, -0.1445, -0.1445, -0.1445, -0.1445],
    [-0.1758, -0.1680, -0.1680, -0.1680, -0.1680, -0.1523, -0.1523, -0.1602, -0.1602, -0.1523]]]])


C++/Libtorch tensor values (img.index({Slice(), Slice(), Slice(None, 10), Slice(None, 10)});):

img: (1,1,.,.) =
  0.3555  0.3555  0.3555  0.3555  0.3555  0.4023  0.3945  0.3867  0.3789  0.3789
  0.3633  0.3633  0.3555  0.3555  0.3555  0.3555  0.3477  0.3555  0.3398  0.3398
  0.3398  0.3320  0.3320  0.3242  0.3398  0.3320  0.3398  0.3242  0.3242  0.3242
  0.2930  0.2930  0.2852  0.2773  0.2852  0.2930  0.2852  0.2852  0.2773  0.2852
  0.2695  0.2695  0.2617  0.2773  0.2695  0.2227  0.2227  0.2227  0.2148  0.2148
  0.1914  0.1914  0.1914  0.1914  0.1914  0.1602  0.1445  0.1289  0.1055  0.0977
  0.1289  0.1133  0.0820  0.0742  0.0586  0.0586  0.0195 -0.0273 -0.0820 -0.0898
  0.0039 -0.0195 -0.0508 -0.0664 -0.0820 -0.1289 -0.1445 -0.1602 -0.1836 -0.1836
 -0.2070 -0.2148 -0.2227 -0.2383 -0.2305 -0.2070 -0.2070 -0.1914 -0.1836 -0.1758
 -0.2539 -0.2461 -0.2461 -0.2383 -0.2305 -0.1914 -0.1914 -0.1758 -0.1680 -0.1602

(1,2,.,.) =
  0.8398  0.8398  0.8242  0.8164  0.8242  0.8555  0.8398  0.8320  0.8242  0.8242
  0.8320  0.8320  0.8242  0.8242  0.8086  0.8008  0.7930  0.7773  0.7695  0.7617
  0.7930  0.7852  0.7773  0.7695  0.7695  0.7695  0.7539  0.7461  0.7305  0.7227
  0.7070  0.7070  0.6992  0.6992  0.6914  0.6836  0.6758  0.6602  0.6523  0.6367
  0.6367  0.6367  0.6289  0.6289  0.6211  0.5664  0.5586  0.5430  0.5352  0.5117
  0.4805  0.4805  0.4805  0.4648  0.4727  0.4258  0.4023  0.3711  0.3555  0.3320
  0.3398  0.3320  0.3008  0.2773  0.2617  0.2461  0.1992  0.1445  0.0898  0.0586
  0.1367  0.1211  0.0898  0.0508  0.0273 -0.0195 -0.0352 -0.0664 -0.0898 -0.1055
 -0.1211 -0.1289 -0.1367 -0.1602 -0.1602 -0.1523 -0.1523 -0.1445 -0.1445 -0.1367
 -0.2148 -0.2070 -0.2070 -0.2070 -0.1992 -0.1680 -0.1680 -0.1602 -0.1523 -0.1445

(1,3,.,.) =
  0.9414  0.9414  0.9336  0.9180  0.9102  0.9336  0.9258  0.9023  0.8945  0.9023
  0.9180  0.9180  0.9102  0.9102  0.8945  0.8711  0.8633  0.8555  0.8242  0.8477
  0.8711  0.8711  0.8633  0.8477  0.8320  0.8164  0.8164  0.7930  0.7852  0.7852
  0.7773  0.7773  0.7539  0.7461  0.7305  0.7148  0.7070  0.6992  0.6836  0.6758
  0.6836  0.6836  0.6758  0.6680  0.6445  0.5898  0.5820  0.5586  0.5508  0.5352
  0.5273  0.5195  0.5117  0.4883  0.4883  0.4414  0.4102  0.3789  0.3633  0.3398
  0.3867  0.3633  0.3320  0.3008  0.2695  0.2539  0.2070  0.1445  0.0898  0.0664
  0.1836  0.1523  0.1133  0.0742  0.0352 -0.0117 -0.0352 -0.0664 -0.0898 -0.1055
 -0.0820 -0.0977 -0.1211 -0.1367 -0.1445 -0.1445 -0.1445 -0.1367 -0.1445 -0.1445
 -0.1758 -0.1758 -0.1758 -0.1758 -0.1758 -0.1602 -0.1523 -0.1680 -0.1602 -0.1602

[ CPUFloatType{1,3,10,10} ]