docstr: Compute the probability of the forward variable alpha.
  
  Args:
      log_probs: Flattened tensor [B, T, U, V+1]
      T: Length of the acoustic sequence T (not padded).
      U: Length of the target sequence U (not padded).
      alphas: Working space memory for alpha of shape [B, T, U].
  
  Returns:
      Loglikelihood of the forward variable alpha.
docstr_tok: ["Compute","the","probability","of","the","forward","variable","alpha",".","Args",":","log_probs",":","Flattened","tensor","[","B",",","T",",","U",",","V+1","]","T",":","Length","of","the","acoustic","sequence","T","(","not","padded",")",".","U",":","Length","of","the","target","sequence","U","(","not","padded",")",".","alphas",":","Working","space","memory","for","alpha","of","shape","[","B",",","T",",","U","]",".","Returns",":","Loglikelihood","of","the","forward","variable","alpha","."]
code: def compute_alphas(
      self,
      log_probs: torch.Tensor,
      T: int,
      U: int,
      alphas: torch.Tensor,
  ):
      """
      Compute the probability of the forward variable alpha.
  
      Args:
          log_probs: Flattened tensor [B, T, U, V+1]
          T: Length of the acoustic sequence T (not padded).
          U: Length of the target sequence U (not padded).
          alphas: Working space memory for alpha of shape [B, T, U].
  
      Returns:
          Loglikelihood of the forward variable alpha.
      """
      idx = CpuRNNT_index(
          U,
          self.maxU_,
          self.minibatch_,
          self.alphabet_size_,
          self.batch_first,
      )
  
      alphas[0] = 0
      for t in range(T):
          for u in range(U):
              if u == 0 and t > 0:
                  alphas[idx(t, 0)] = (
                      alphas[idx(t - 1, 0)]
                      + log_probs[idx(t - 1, 0) * 2]
                  )
  
              if t == 0 and u > 0:
                  alphas[idx(0, u)] = (
                      alphas[idx(0, u - 1)]
                      + log_probs[idx(0, u - 1) * 2 + 1]
                  )
  
              if t > 0 and u > 0:
                  no_emit = (
                      alphas[idx(t - 1, u)]
                      + log_probs[idx(t - 1, u) * 2]
                  )
                  emit = (
                      alphas[idx(t, u - 1)]
                      + log_probs[idx(t, u - 1) * 2 + 1]
                  )
                  alphas[idx(t, u)] = log_sum_exp(emit, no_emit)
  
      loglike = (
          alphas[idx(T - 1, U - 1)] + log_probs[idx(T - 1, U - 1) * 2]
      )
      return loglike
  
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 3 for calling the CPURNNT obj's underlying member method code with example usage: obj.compute_alphas(log_probs, T, U, alphas) and returns: loglike