code: def compute_costs_data(
      source: torch.Tensor, dest: torch.Tensor, fastemit_lambda: float
  ):
      block = cuda.blockIdx.x
      tid = cuda.threadIdx.x
      idx = block * cuda.blockDim.x + tid
      length = source.shape[0]
  
      if idx < length:
          copy_data_1d(source, dest, idx)
          dest[idx] *= -1.0
          dest[idx] *= 1.0 + fastemit_lambda
  
file_path: espnet/espnet2/asr/transducer/rnnt_multi_blank/utils/rnnt_helper.py
imports: import math
  from typing import Optional, Tuple
  import torch
  from numba import cuda
  from espnet2.asr.transducer.rnnt_multi_blank.utils import global_constants
label_desc: Use image node_id 10 for calling a global function with example usage: compute_costs_data(source, dest, fastemit_lambda) without return types