code: def test_case_big_tensor(
      self, device, k2_is_appropriate, k2_cuda_is_enabled
  ):
      skip_test_if_unsupported(
          device, k2_is_appropriate, k2_cuda_is_enabled
      )
  
      # minibatch x T x U x alphabet_size
      acts = [
          [
              [
                  [
                      0.06535690384862791,
                      0.7875301411923206,
                      0.08159176605666074,
                  ],
                  [
                      0.5297155426466327,
                      0.7506749639230854,
                      0.7541348379087998,
                  ],
                  [
                      0.6097641124736383,
                      0.8681404965673826,
                      0.6225318186056529,
                  ],
              ],
              [
                  [
                      0.6685222872103057,
                      0.8580392805336061,
                      0.16453892311765583,
                  ],
                  [
                      0.989779515236694,
                      0.944298460961015,
                      0.6031678586829663,
                  ],
                  [
                      0.9467833543605416,
                      0.666202507295747,
                      0.28688179752461884,
                  ],
              ],
              [
                  [
                      0.09418426230195986,
                      0.3666735970751962,
                      0.736168049462793,
                  ],
                  [
                      0.1666804425271342,
                      0.7141542198635192,
                      0.3993997272216727,
                  ],
                  [
                      0.5359823524146038,
                      0.29182076440286386,
                      0.6126422611507932,
                  ],
              ],
              [
                  [
                      0.3242405528768486,
                      0.8007644367291621,
                      0.5241057606558068,
                  ],
                  [
                      0.779194617063042,
                      0.18331417220174862,
                      0.113745182072432,
                  ],
                  [
                      0.24022162381327106,
                      0.3394695622533106,
                      0.1341595066017014,
                  ],
              ],
          ],
          [
              [
                  [
                      0.5055615569388828,
                      0.051597282072282646,
                      0.6402903936686337,
                  ],
                  [
                      0.43073311517251,
                      0.8294731834714112,
                      0.1774668847323424,
                  ],
                  [
                      0.3207001991262245,
                      0.04288308912457006,
                      0.30280282975568984,
                  ],
              ],
              [
                  [
                      0.6751777088333762,
                      0.569537369330242,
                      0.5584738347504452,
                  ],
                  [
                      0.08313242153985256,
                      0.06016544344162322,
                      0.10795752845152584,
                  ],
                  [
                      0.7486153608562472,
                      0.943918041459349,
                      0.4863558118797222,
                  ],
              ],
              [
                  [
                      0.4181986264486809,
                      0.6524078485043804,
                      0.024242983423721887,
                  ],
                  [
                      0.13458171554507403,
                      0.3663418070512402,
                      0.2958297395361563,
                  ],
                  [
                      0.9236695822497084,
                      0.6899291482654177,
                      0.7418981733448822,
                  ],
              ],
              [
                  [
                      0.25000547599982104,
                      0.6034295486281007,
                      0.9872887878887768,
                  ],
                  [
                      0.5926057265215715,
                      0.8846724004467684,
                      0.5434495396894328,
                  ],
                  [
                      0.6607698886038497,
                      0.3771277082495921,
                      0.3580209022231813,
                  ],
              ],
          ],
      ]
  
      expected_costs = [4.2806528590890736, 3.9384369822503591]
      expected_grads = [
          [
              [
                  [-1.86843902e-01, -6.25548810e-02, 2.49398798e-01],
                  [-2.03376666e-01, 2.02399328e-01, 9.77333169e-04],
                  [-1.41016081e-01, 7.91234672e-02, 6.18926100e-02],
              ],
              [
                  [-1.15517676e-02, -8.12802389e-02, 9.28319991e-02],
                  [-1.54257029e-01, 2.29432687e-01, -7.51756504e-02],
                  [-2.46593088e-01, 1.46404594e-01, 1.00188486e-01],
              ],
              [
                  [-1.29182907e-02, -6.15932420e-02, 7.45115355e-02],
                  [-5.59857301e-02, 2.19830811e-01, -1.63845062e-01],
                  [-4.97626871e-01, 2.09239945e-01, 2.88386941e-01],
              ],
              [
                  [1.36048580e-02, -3.02196294e-02, 1.66147724e-02],
                  [1.13924511e-01, 6.27811998e-02, -1.76705718e-01],
                  [-6.67078257e-01, 3.67658824e-01, 2.99419403e-01],
              ],
          ],
          [
              [
                  [-3.56343776e-01, -5.53474613e-02, 4.11691219e-01],
                  [-9.69219357e-02, 2.94591039e-02, 6.74628317e-02],
                  [-6.35175705e-02, 2.76544970e-02, 3.58630717e-02],
              ],
              [
                  [-1.54499024e-01, -7.39420280e-02, 2.28441030e-01],
                  [-1.66789949e-01, -8.78955179e-05, 1.66877866e-01],
                  [-1.72369644e-01, 1.05565332e-01, 6.68043196e-02],
              ],
              [
                  [2.38748826e-02, -1.18255816e-01, 9.43809375e-02],
                  [-1.04707085e-01, -1.08934477e-01, 2.13641584e-01],
                  [-3.69844258e-01, 1.80118099e-01, 1.89726159e-01],
              ],
              [
                  [2.57137045e-02, -7.94617534e-02, 5.37480488e-02],
                  [1.22328237e-01, -2.38788679e-01, 1.16460443e-01],
                  [-5.98686993e-01, 3.02203178e-01, 2.96483815e-01],
              ],
          ],
      ]
  
      acts = np.array(acts)
      expected_costs = np.array(expected_costs)
      labels = [[1, 2], [1, 1]]
  
      fn_k2 = init_k2_rnnt(
          num_classes=acts.shape[-1], blank=0, reduction="none"
      )
      k2_costs, k2_grads = wrap_and_call(fn_k2, acts, labels, device)
  
      assert np.allclose(
          k2_costs, expected_costs
      ), "big_test average costs mismatch."
      assert np.allclose(
          k2_grads, expected_grads, rtol=1e-3
      ), "big_test grads for average cost mismatch."
  
file_path: NeMo/tests/collections/asr/k2/test_rnnt.py
imports: import numpy
  import pytest
  import torch
  from nemo.collections.asr.parts.numba.rnnt_loss.rnnt_numpy import RNNTLoss
label_desc: Use image node_id 4 for calling the TestRNNTLossK2 obj's underlying member method code with example usage: obj.test_case_big_tensor(device, k2_is_appropriate, k2_cuda_is_enabled) without return types