So I have an image of size 9x50 as:

tensor([[ 0.4115, -0.6465, -1.6343,  0.6694, -0.8929,  0.7482, -0.6784, -1.2556,
-0.9919,  0.7736, -1.3033, -1.4822,  1.6883,  1.3857, -0.4635, -0.4117,
0.1361,  1.2751,  1.5286, -1.0493,  0.4839, -2.1620, -1.4373, -0.3013,
0.5121,  0.7913,  0.7924, -0.7720, -0.3467,  1.1353,  0.5904, -1.8757,
0.5789, -2.0829,  1.2716, -0.2533, -0.6339,  0.5726, -0.1584,  1.2937,
-0.6060, -0.7181, -1.1443,  0.1927,  0.0326, -1.3743, -0.5325,  0.7743,
-1.0776,  0.5832],
[-0.4022, -0.0806,  0.6202,  1.4176, -0.0325,  0.2146,  0.4789,  0.2615,
-1.9354, -0.9925, -1.3699,  1.4623,  1.1422,  0.4273,  0.7865,  0.4704,
0.7516, -0.8715, -0.7594, -0.3551,  0.6217,  1.5333, -1.7359,  0.7198,
-0.4480,  0.4198,  0.5431,  0.2605, -0.5880, -0.3684,  0.5031, -1.3644,
0.3791,  0.4395, -0.0098, -0.3250, -1.9895,  0.5293,  0.5274,  1.5332,
1.0197, -1.1839,  0.2819,  1.7081,  0.1653,  0.3076, -1.0679, -0.5644,
2.5712, -0.6777],
[ 0.3608,  0.7212, -1.5474,  1.0859, -0.5586,  1.3594, -1.2196, -1.5036,
0.8116,  0.6708,  0.9988, -0.7967, -0.7120,  0.5176, -1.9599,  0.2420,
0.0513, -1.1133,  0.6954, -0.4826, -1.5786,  0.1810,  0.7230,  0.4276,
-0.2598,  0.4369, -0.3106, -0.0446,  1.1185, -0.7355,  0.0219, -0.0619,
0.0329,  0.1079,  0.2461,  0.7204,  1.0873, -1.1423,  0.0986, -0.6493,
1.1245, -0.8159,  1.3520, -0.8926,  0.4020,  1.0555, -1.1234, -0.0147,
1.3508,  0.6182],
[-0.7430,  0.5251, -0.6153, -0.0003, -0.6046,  1.1388, -0.7799, -1.9012,
-0.4144, -0.0861,  0.0823,  0.6609,  1.0585, -0.5026, -0.1830,  0.8965,
0.1796,  0.7578,  0.2869, -1.3962, -1.7420,  1.7718,  1.6606,  0.5634,
-0.1225,  0.5426, -2.1004,  0.0133,  0.7839,  1.8201, -0.0306,  0.2149,
-0.2372, -0.3642,  0.3713,  0.1301, -0.2877, -0.4470, -0.1347, -1.3249,
0.6950,  0.0947, -0.0682, -0.3107, -0.5063, -0.1554,  0.5312,  0.2986,
-0.7677,  0.9213],
[ 1.1000, -0.6128, -0.6937, -0.9583,  0.2561, -0.0408,  0.5273, -0.1111,
-0.3420,  0.9789, -0.5763, -0.3564, -1.1349,  0.2419,  1.0597,  1.3880,
0.3580, -1.2515, -0.1734,  0.2403, -0.2600,  0.4373, -0.5632,  0.5021,
1.9840, -0.5519, -1.5868,  1.2105,  1.0267,  1.4813, -1.5021,  1.6625,
-0.9624,  1.4024,  2.0388,  0.0238,  0.3076,  1.6528, -0.4595, -0.7159,
-0.8997, -1.8804, -1.1647,  1.8108, -1.4731, -1.1084,  0.5496,  1.5376,
0.1698, -0.4175],
[-0.7766, -2.0425, -0.8977,  0.0425,  1.8165, -0.6411,  0.1768, -0.7219,
-0.4880, -0.4142,  0.7928, -0.5951,  1.1639, -0.0928, -0.3169,  1.5937,
-1.1871,  0.2590, -0.1274,  0.1017, -1.0488,  0.1753,  0.5793,  0.1125,
-0.4837, -1.4312,  0.0187, -0.6604, -0.3871,  1.6479, -1.4328,  0.9142,
0.0699,  0.8660,  1.0728, -0.8291,  1.0222,  0.1272, -0.5531,  0.8532,
0.5304,  0.4040, -0.7247,  0.1954, -0.2499,  0.9694,  0.8410, -0.1247,
-1.5646, -1.3319],
[-0.2229, -1.6662,  0.5105, -0.2770,  0.3966,  1.0326,  0.9928, -0.4494,
0.6234,  0.4386, -0.6726,  1.1923,  1.1223, -0.5312,  0.2890, -0.8353,
-1.3872, -0.2604,  1.7785,  0.2281,  0.8691,  0.8132, -0.0213, -1.0649,
0.3980, -0.2038,  1.5023,  0.3054,  0.8736,  1.8556, -1.3965,  1.0579,
-0.0868, -0.3515, -1.2344, -0.2689,  1.1425,  0.1928,  1.0721, -1.5331,
-0.2131,  1.0340,  1.6211,  0.2218,  1.7555,  0.3581,  2.6108, -0.1747,
0.1864,  0.0211],
[-2.1773,  0.4278,  0.2847,  0.4405,  0.9457, -0.1819, -0.3713,  1.0402,
-0.9497, -0.0645,  0.1729, -0.6848,  0.2156, -0.0078,  0.3848, -0.4249,
1.2975, -0.4167,  0.0660,  1.6326, -0.4543,  0.7339,  0.6010,  0.8946,
1.2881, -1.0936,  1.1421, -0.5225,  0.1843, -1.0033,  0.1155, -0.4692,
1.5356,  0.1045, -1.0899,  2.0136,  1.7887,  2.1656, -1.2265, -0.0519,
0.0472,  0.2626, -0.5554,  1.6628, -1.0357,  0.4898,  1.1277,  0.0699,
0.4967,  0.8722],
[ 0.7352, -0.6486, -0.6952,  2.6622,  0.2339, -0.0961,  1.8036, -0.3650,
-1.2539, -0.0111,  0.6007, -0.3418, -0.9551,  0.3020,  1.3864, -0.0676,
0.8362,  0.1694, -0.5506,  0.4202,  0.2058, -0.9739,  1.5484, -1.0143,
-0.7052, -0.2831,  0.7834, -3.0195,  0.3679,  0.9377,  0.0174, -1.4630,
-0.4082, -0.5332,  0.8701, -0.5404,  1.8485, -1.9600, -0.1757, -0.1020,
-0.1524, -1.8317, -0.0961,  1.1949, -1.2083,  1.7236, -0.2691,  0.9958,
0.6578, -0.0425]], grad_fn=)

and it has labels of size 9x1 like so,

[3, 3, 7, 0, 8, 3, 3, 3, 1],

between 0-8 (no one hot encoding)
which means each row of 50 dimensions in this image has a label. To summarize,
image

9x50 --> 9x1

and, we have a batch size of 128.
So, 128x9x50 maps to 128x9.
Since, each image has 9 labels, can we apply 2d crossentropy? Am not sure how to use that though.
OR , do you suggest some other loss function? What about the accuracy calculation? I guess I may not be able to use hamming distance since am not using one-hot-encoded label vectors. my labels are class indexes
Regards