code: def test_case_small_random(self, device, dtype):
      if device == "cuda":
          numba_utils.skip_numba_cuda_test_if_unsupported(
              __NUMBA_MINIMUM_VERSION__
          )
  
      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
  
      rng = np.random.RandomState(0)
      acts = rng.randn(1, 4, 3, 3).astype(dtype)
      labels = [[1, 2]]
  
      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)
  
      assert np.allclose(
          pt_cost, np_cost, atol=cost_threshold, rtol=rtol
      ), "small_random_test costs mismatch."
      assert np.allclose(
          pt_grads, np_grads, atol=grad_threshold, rtol=rtol
      ), "small_random_test gradient mismatch."
  
      assert np.allclose(
          pt_cost, ag_cost, atol=cost_threshold, rtol=rtol
      ), "small_random_test costs mismatch."
      assert np.allclose(
          pt_grads, ag_grads, atol=grad_threshold, rtol=rtol
      ), "small_random_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 2 for calling the TestRNNTLossPytorch obj's underlying member method code with example usage: obj.test_case_small_random(device, dtype) without return types