code: def test_case_small_random_accumulated(self, device):
      if device == "cuda":
          numba_utils.skip_numba_cuda_test_if_unsupported(
              __NUMBA_MINIMUM_VERSION__
          )
  
      torch.manual_seed(0)
      base_layer = torch.randn(3, 5, requires_grad=True)
  
      mid1 = torch.randn(1, 4, 3, 3, requires_grad=True)
      labels1 = [[1, 3]]
  
      mid2 = torch.randn(1, 6, 5, 3, requires_grad=True)
      labels2 = [[1, 2, 3, 4]]
  
      def zero_grad():
          if base_layer.grad is not None:
              base_layer.grad = None
          if mid1.grad is not None:
              mid1.grad = None
          if mid2.grad is not None:
              mid2.grad = None
  
      fn_pt = RNNTLossNumba(blank=0, reduction="sum")
      fn_np = RNNTLoss_Numpy()
  
      # run 1
      acts1 = torch.matmul(mid1, base_layer)  # [1, 4, 3, 5]
      pt_cost1, _ = wrap_and_call(fn_pt, acts1, labels1, device)
      pt_grads1 = base_layer.grad.detach().cpu().numpy()
  
      zero_grad()
  
      acts1 = torch.matmul(mid1, base_layer)  # [1, 4, 3, 5]
      np_cost1, _ = wrap_and_call(fn_np, acts1, labels1, device)
      np_grads1 = base_layer.grad.detach().cpu().numpy()
  
      zero_grad()
  
      assert np.allclose(pt_grads1, np_grads1, atol=1e-6)
  
      # run 2
      acts2 = torch.matmul(mid2, base_layer)  # [1, 4, 3, 5]
      pt_cost2, _ = wrap_and_call(fn_pt, acts2, labels2, device)
      pt_grads2 = base_layer.grad.clone().cpu().numpy()
  
      zero_grad()
  
      acts2 = torch.matmul(mid2, base_layer)  # [1, 4, 3, 5]
      np_cost2, _ = wrap_and_call(fn_np, acts2, labels2, device)
      np_grads2 = base_layer.grad.clone().cpu().numpy()
  
      zero_grad()
  
      assert np.allclose(pt_grads2, np_grads2, atol=1e-6)
  
      # run 1 + 2
      acts1 = torch.matmul(mid1, base_layer)  # [1, 4, 3, 5]
      pt_cost1, _ = wrap_and_call(fn_pt, acts1, labels1, device)
  
      acts2 = torch.matmul(mid2, base_layer)  # [1, 6, 5, 5]
      pt_cost2, _ = wrap_and_call(fn_pt, acts2, labels2, device)
      pt_grads1_p_2 = base_layer.grad.clone().cpu().numpy()
  
      assert np.allclose(
          pt_grads1_p_2, np_grads1 + np_grads2, atol=1e-5
      )
  
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 8 for calling the TestRNNTLossPytorch obj's underlying member method code with example usage: obj.test_case_small_random_accumulated(device) without return types