docstr: A placeholder Index computation class that emits the resolved index in a
  flattened tensor, mimicing pointer indexing in CUDA kernels on the CPU.
  
  Args:
      U: Length of the current target sample (without padding).
      maxU: Max Length of the padded target samples.
      minibatch: Minibatch index
      alphabet_size: Size of the vocabulary including RNNT blank - V+1.
      batch_first: Bool flag determining if batch index is first or third.
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code: def __init__(
      self,
      U: int,
      maxU: int,
      minibatch: int,
      alphabet_size: int,
      batch_first: bool,
  ):
      """
      A placeholder Index computation class that emits the resolved index in a
      flattened tensor, mimicing pointer indexing in CUDA kernels on the CPU.
  
      Args:
          U: Length of the current target sample (without padding).
          maxU: Max Length of the padded target samples.
          minibatch: Minibatch index
          alphabet_size: Size of the vocabulary including RNNT blank - V+1.
          batch_first: Bool flag determining if batch index is first or third.
      """
  
      super(CpuRNNT_index, self).__init__()
      self.U = U
      self.maxU = maxU
      self.minibatch = minibatch
      self.alphabet_size = alphabet_size
      self.batch_first = batch_first
  
imports: import math
  import multiprocessing
  from typing import Optional
  import numba
  import torch
  from torch.autograd import Function
  from espnet2.asr.transducer.rnnt_multi_blank.utils import global_constants
label_desc: Use image node_id 1 to create a new CpuRNNT_index object  with example: obj = CpuRNNT_index(U, maxU, minibatch, alphabet_size, batch_first)