code: def logp(
      denom: torch.Tensor,
      acts: torch.Tensor,
      maxT: int,
      maxU: int,
      alphabet_size: int,
      mb: int,
      t: int,
      u: int,
      v: int,
  ):
      """
      Compute the sum of log probability from the activation tensor and its denominator.
  
      Args:
          denom: Tensor of shape [B, T, U] flattened. Represents the denominator of the
              logprobs activation tensor across entire vocabulary.
          acts: Tensor of shape [B, T, U, V+1] flattened.
              Represents the logprobs activation tensor.
          maxT: The maximum possible acoustic sequence length.
              Represents T in the logprobs tensor.
          maxU: The maximum possible target sequence length.
              Represents U in the logprobs tensor.
          alphabet_size: The vocabulary dimension V+1 (inclusive of RNNT blank).
          mb: Batch indexer.
          t: Acoustic sequence timestep indexer.
          u: Target sequence timestep indexer.
          v: Vocabulary token indexer.
  
      Returns:
          The sum of logprobs[mb, t, u, v] + denom[mb, t, u]
      """
  
      col = (mb * maxT + t) * maxU + u
      return denom[col] + acts[col * alphabet_size + v]
  
file_path: espnet/espnet2/asr/transducer/rnnt_multi_blank/utils/cuda_utils/gpu_rnnt_kernel.py
imports: import math
  import torch
  from numba import cuda
  from espnet2.asr.transducer.rnnt_multi_blank.utils import rnnt_helper
label_desc: Use image node_id 1 for calling a global function with example usage: logp(denom, acts, maxT, maxU, alphabet_size, mb, t, u, v) and returns: unknown