No, they werent deleted, but the translated and normalized joints replaced the original joints in self.all_joints
For example,
This is the first iteration of the ground truth at __getitem__(1) after doing some random augmentation (accurate values):
tensor([ 48.5065,  66.6261,  54.1668,  47.8129,  56.8132,  38.7390,
         58.9134,  31.6378,  60.9088,  24.9512,  47.4044,  45.5503,
         48.5107,  34.2860,  49.2876,  26.3807,  50.0234,  18.8972,
         41.2396,  45.7394,  40.1532,  35.9195,  39.3954,  29.1414,
         38.6335,  22.4301,  34.0548,  48.8389,  31.3979,  42.1586,
         29.6963,  37.8616,  27.8077,  33.0767,  54.5841,  63.2553,
         60.8536,  58.3544,  65.4259,  53.5492,  71.3503,  49.6988], dtype=torch.float64)

calling __getitem__(1) again gives you a smaller number as the random augmentation were performed on the above tensor instead of the original ground truth
tensor([  3.8037,  30.5698,   6.0577,  23.0783,   7.1115,  19.4650,
          7.9478,  16.6372,   8.7423,  13.9746,   3.3648,  22.1773,
          3.8053,  17.6918,   4.1147,  14.5438,   4.4077,  11.5639,
          0.9099,  22.2526,   0.4773,  18.3422,   0.1756,  15.6432,
         -0.1278,  12.9707,  -1.9511,  23.4868,  -3.0091,  20.8267,
         -3.6867,  19.1156,  -4.4387,  17.2102,   6.2238,  29.2275,
          8.7204,  27.2759,  10.5411,  25.3625,  12.9002,  23.8292], dtype=torch.float64)

and again __getitem__(1)
tensor([-13.9973,  16.2119, -13.0997,  13.2288, -12.6801,  11.7899,
        -12.3471,  10.6639, -12.0307,   9.6036, -14.1720,  12.8700,
        -13.9966,  11.0838, -13.8734,   9.8303, -13.7567,   8.6436,
        -15.1496,  12.9000, -15.3219,  11.3428, -15.4420,  10.2681,
        -15.5628,   9.2038, -16.2889,  13.3914, -16.7102,  12.3322,
        -16.9800,  11.6508, -17.2795,  10.8921, -13.0336,  15.6774,
        -12.0394,  14.9003, -11.3144,  14.1384, -10.3750,  13.5278], dtype=torch.float64)

the ground truths were iteratively replaced with every call of that index, which led to really really bad training