code: def cost_and_grad_kernel(
      self,
      log_probs: torch.Tensor,
      grad: torch.Tensor,
      labels: torch.Tensor,
      mb: int,
      T: int,
      U: int,
      bytes_used: int,
  ):
      idx = CpuRNNT_index(
          U,
          self.maxU_,
          self.minibatch_,
          self.alphabet_size_,
          self.batch_first,
      )
      rnntm = CpuRNNT_metadata(
          T,
          U,
          self.workspace,
          bytes_used,
          self.blank_,
          labels,
          log_probs,
          idx,
      )
  
      if self.batch_first:
          # zero grads
          grad *= 0.0
  
      llForward = self.compute_alphas(
          rnntm.log_probs2, T, U, rnntm.alphas
      )
      llBackward = self.compute_betas_and_grads(
          grad,
          rnntm.log_probs2,
          T,
          U,
          rnntm.alphas,
          rnntm.betas,
          labels,
          llForward,
      )
  
      # Scale llForward by FastEmit lambda
      llForward += llForward * self.fastemit_lambda_
      llBackward += llBackward * self.fastemit_lambda_
  
      diff = (llForward - llBackward).abs()
      if diff > 0.1:
          print(
              f"WARNING: Forward backward likelihood mismatch : {diff}"
          )
  
      return -llForward
  
imports: import math
  import multiprocessing
  from typing import Optional
  import numba
  import torch
  from torch.autograd import Function
  from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants
label_desc: Use image node_id 2 for calling the CPURNNT obj's underlying member method code with example usage: obj.cost_and_grad_kernel(log_probs, grad, labels, mb, T, U, bytes_used) and returns: unknown