code: def test_case_small(self, device, dtype):
      if device == "cuda":
          numba_utils.skip_numba_cuda_test_if_unsupported(
              __NUMBA_MINIMUM_VERSION__
          )
  
      acts = np.array(
          [
              [
                  [
                      [0.1, 0.6, 0.1, 0.1, 0.1],
                      [0.1, 0.1, 0.6, 0.1, 0.1],
                      [0.1, 0.1, 0.2, 0.8, 0.1],
                  ],
                  [
                      [0.1, 0.6, 0.1, 0.1, 0.1],
                      [0.1, 0.1, 0.2, 0.1, 0.1],
                      [0.7, 0.1, 0.2, 0.1, 0.1],
                  ],
              ]
          ]
      ).astype(dtype)
      labels = [[1, 2]]
  
      cost_threshold = 1e-8 if dtype == np.float32 else 5e-4
      grad_threshold = 1e-8 if dtype == np.float32 else 1e-4
      rtol = 1e-5 if dtype == np.float32 else 1e-3
  
      fn_pt = RNNTLossNumba(blank=0, reduction="sum")
      pt_cost, pt_grads = wrap_and_call(fn_pt, acts, labels, device)
  
      fn_np = RNNTLoss_Numpy()
      np_cost, np_grads = wrap_and_call(fn_np, acts, labels, device)
  
      fn_ag = RNNTLossPytorch(
          blank=0, reduction="sum"
      )  # ag for automatic gradient computation
      ag_cost, ag_grads = wrap_and_call(fn_ag, acts, labels, device)
  
      expected_cost = 4.495666
      expected_grads = np.array(
          [
              [
                  [
                      [
                          -0.13116688,
                          -0.3999269,
                          0.17703125,
                          0.17703125,
                          0.17703125,
                      ],
                      [
                          -0.18572757,
                          0.12247056,
                          -0.18168412,
                          0.12247056,
                          0.12247056,
                      ],
                      [
                          -0.32091254,
                          0.06269141,
                          0.06928472,
                          0.12624499,
                          0.06269141,
                      ],
                  ],
                  [
                      [
                          0.05456069,
                          -0.21824276,
                          0.05456069,
                          0.05456069,
                          0.05456069,
                      ],
                      [
                          0.12073959,
                          0.12073959,
                          -0.48295835,
                          0.12073959,
                          0.12073959,
                      ],
                      [
                          -0.6925882,
                          0.16871116,
                          0.18645467,
                          0.16871116,
                          0.16871116,
                      ],
                  ],
              ]
          ]
      )
  
      assert np.allclose(
          pt_cost, expected_cost, atol=cost_threshold, rtol=1e-6
      ), "small_test costs mismatch."
      assert np.allclose(
          pt_grads, expected_grads, atol=grad_threshold, rtol=rtol
      ), "small_test gradient mismatch."
  
      assert np.allclose(
          pt_cost, np_cost, atol=cost_threshold, rtol=rtol
      ), "small_test costs mismatch."
      assert np.allclose(
          pt_grads, np_grads, atol=grad_threshold, rtol=rtol
      ), "small_test gradient mismatch."
  
      assert np.allclose(
          ag_cost, np_cost, atol=cost_threshold, rtol=rtol
      ), "small_test costs mismatch."
      assert np.allclose(
          ag_grads, np_grads, atol=cost_threshold, rtol=rtol
      ), "small_test gradient mismatch."
  
imports: import random
  import numpy
  import pytest
  import torch
  from nemo.collections.asr.losses.rnnt import MultiblankRNNTLossPytorch, RNNTLossPytorch, TDTLossPytorch
  from nemo.collections.asr.parts.numba.rnnt_loss.rnnt_numpy import RNNTLoss
  from nemo.collections.asr.parts.numba.rnnt_loss.rnnt_pytorch import MultiblankRNNTLossNumba, RNNTLossNumba, TDTLossNumba
  from nemo.core.utils import numba_utils
  from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
label_desc: Use image node_id 1 for calling the TestRNNTLossPytorch obj's underlying member method code with example usage: obj.test_case_small(device, dtype) without return types