code: def test_case_small_fastemit_clamp(
      self, device, dtype, fastemit_lambda
  ):
      if device == "cuda":
          numba_utils.skip_numba_cuda_test_if_unsupported(
              __NUMBA_MINIMUM_VERSION__
          )
  
      GRAD_CLAMP = 0.1
      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 1e-3
      grad_threshold = 1e-8 if dtype == np.float32 else 5e-4
      rtol = 1e-5 if dtype == np.float32 else 1e-3
  
      fn_pt = RNNTLossNumba(
          blank=0,
          reduction="sum",
          fastemit_lambda=fastemit_lambda,
          clamp=GRAD_CLAMP,
      )
      pt_cost, pt_grads = wrap_and_call(fn_pt, acts, labels, device)
  
      fn_np = RNNTLoss_Numpy(
          blank=0, fastemit_lambda=fastemit_lambda, clamp=GRAD_CLAMP
      )
      np_cost, np_grads = wrap_and_call(fn_np, acts, labels, device)
  
      expected_cost = 4.495666
      expected_cost += expected_cost * fastemit_lambda
  
      assert np.allclose(
          pt_cost, expected_cost, atol=cost_threshold, rtol=rtol
      ), "small_test costs 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."
  
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 7 for calling the TestRNNTLossPytorch obj's underlying member method code with example usage: obj.test_case_small_fastemit_clamp(device, dtype, fastemit_lambda) without return types