code: def get_sample_small_blank_last(cls) -> "RnntLossSampleData":
      activations = np.array(
          [
              [
                  [
                      [0.0, 1.0, 3.0],
                      [0.0, 2.0, 3.0],
                      [1.0, 1.0, 3.0],
                      [2.0, 3.0, 2.0],
                  ],
                  [
                      [0.0, 0.0, 1.0],
                      [0.0, 1.0, 1.0],
                      [1.0, 0.0, 1.0],
                      [2.0, 2.0, 0.0],
                  ],
                  [
                      [0.0, 2.0, 5.0],
                      [0.0, 3.0, 5.0],
                      [1.0, 2.0, 5.0],
                      [2.0, 4.0, 4.0],
                  ],
                  [
                      [0.0, 3.0, 4.0],
                      [0.0, 4.0, 4.0],
                      [1.0, 3.0, 4.0],
                      [2.0, 5.0, 3.0],
                  ],
                  [
                      [2.0, 2.0, 1.0],
                      [2.0, 3.0, 1.0],
                      [3.0, 2.0, 1.0],
                      [4.0, 4.0, 0.0],
                  ],
              ]
          ]
      )
      labels = np.array([[0, 1, 0]])
  
      expected_cost = [6.789285182952881]
      expected_grads = np.array(
          [
              [
                  [
                      [
                          -0.03551076725125313,
                          0.11419519782066345,
                          -0.07868456840515137,
                      ],
                      [
                          0.0027224558871239424,
                          0.00704305712133646,
                          -0.009765520691871643,
                      ],
                      [
                          0.0013856772566214204,
                          0.0013924005907028913,
                          -0.0027780719101428986,
                      ],
                      [
                          1.4249643527364242e-06,
                          3.873454716085689e-06,
                          -5.298420546751004e-06,
                      ],
                  ],
                  [
                      [
                          -0.1934257447719574,
                          0.19551163911819458,
                          -0.0020859241485595703,
                      ],
                      [
                          0.07043898105621338,
                          0.05738453567028046,
                          -0.12782356142997742,
                      ],
                      [
                          0.061031512916088104,
                          0.02286236733198166,
                          -0.08389391005039215,
                      ],
                      [
                          0.0005252412520349026,
                          0.0005252412520349026,
                          -0.0010504829697310925,
                      ],
                  ],
                  [
                      [
                          -0.007841046899557114,
                          0.025142310187220573,
                          -0.017301201820373535,
                      ],
                      [
                          0.0019501042552292347,
                          0.0005148053169250488,
                          -0.0024650096893310547,
                      ],
                      [
                          0.0027856370434165,
                          0.008609085343778133,
                          -0.01139475405216217,
                      ],
                      [
                          9.526080975774676e-05,
                          0.0007038871408440173,
                          -0.000799147819634527,
                      ],
                  ],
                  [
                      [
                          -0.01533521432429552,
                          0.1386115401983261,
                          -0.12327653169631958,
                      ],
                      [
                          0.002850571647286415,
                          -0.006693005561828613,
                          0.003842458128929138,
                      ],
                      [
                          0.009236274287104607,
                          0.08995233476161957,
                          -0.0991886705160141,
                      ],
                      [
                          0.0001865450612967834,
                          0.0037468576338142157,
                          -0.003933403175324202,
                      ],
                  ],
                  [
                      [
                          -0.2888762652873993,
                          0.211185485124588,
                          0.07769080251455307,
                      ],
                      [
                          0.15952755510807037,
                          -0.2182144820690155,
                          0.05868690833449364,
                      ],
                      [
                          -0.3332723379135132,
                          0.2436419129371643,
                          0.0896308496594429,
                      ],
                      [
                          0.4954628646373749,
                          0.4954628646373749,
                          -0.9909257292747498,
                      ],
                  ],
              ]
          ]
      )
      return RnntLossSampleData(
          vocab_size=3,
          blank_id=2,
          logits=torch.from_numpy(activations).to(torch.float32),
          targets=torch.from_numpy(labels),
          input_lengths=torch.tensor([5]),
          target_lengths=torch.tensor([3]),
          expected_cost=torch.tensor(expected_cost).to(torch.float32),
          expected_grads=torch.from_numpy(expected_grads),
      )
  
file_path: NeMo/tests/collections/asr/conftest.py
imports: from dataclasses import dataclass
  from typing import Optional, Type
  import numpy
  import pytest
  import torch
label_desc: Use image node_id 2 for calling the RnntLossSampleData obj's underlying member method code with example usage: obj.get_sample_small_blank_last(cls) and returns: RnntLossSampleData