docstr: Helper method that uses the workspace and constructs slices of it that
      can be used.
  
  Returns:
      An int, representing the offset of the used workspace (practically,
      the slice of the workspace consumed) A tuple of tensors representing
      the shared workspace.
docstr_tok: ["Helper","method","that","uses","the","workspace","and","constructs","slices","of","it","that","can","be","used",".","Returns",":","An","int",",","representing","the","offset","of","the","used","workspace","(","practically",",","the","slice","of","the","workspace","consumed",")","A","tuple","of","tensors","representing","the","shared","workspace","."]
code: def _prepare_workspace(self) -> (int, Tuple[torch.Tensor]):
      """
      Helper method that uses the workspace and constructs slices of it that
          can be used.
  
      Returns:
          An int, representing the offset of the used workspace (practically,
          the slice of the workspace consumed) A tuple of tensors representing
          the shared workspace.
      """
  
      used_offset = 0
  
      # // denom
      denom = self.gpu_workspace[
          used_offset : used_offset
          + self.maxT_ * self.maxU_ * self.minibatch_
      ]
      used_offset += self.maxT_ * self.maxU_ * self.minibatch_
  
      # // alphas & betas
      alphas = self.gpu_workspace[
          used_offset : used_offset
          + self.maxT_ * self.maxU_ * self.minibatch_
      ]
      used_offset += self.maxT_ * self.maxU_ * self.minibatch_
      betas = self.gpu_workspace[
          used_offset : used_offset
          + self.maxT_ * self.maxU_ * self.minibatch_
      ]
      used_offset += self.maxT_ * self.maxU_ * self.minibatch_
  
      # // logllh
      llForward = self.gpu_workspace[
          used_offset : used_offset + self.minibatch_
      ]
      used_offset += self.minibatch_
      llBackward = self.gpu_workspace[
          used_offset : used_offset + self.minibatch_
      ]
      used_offset += self.minibatch_
  
      bigblank_durations = self.big_blank_workspace[
          : self.num_big_blanks
      ]
  
      return used_offset, (
          denom,
          alphas,
          betas,
          llForward,
          llBackward,
          bigblank_durations,
      )
  
imports: import multiprocessing
  from typing import Optional, Tuple
  import numba
  import torch
  from numba import cuda
  from espnet2.asr.transducer.rnnt_multi_blank.utils import global_constants, rnnt_helper
  from espnet2.asr.transducer.rnnt_multi_blank.utils.cuda_utils import gpu_rnnt_kernel, reduce
label_desc: Use image node_id 5 for calling the MultiblankGPURNNT obj's underlying member method code with example usage: obj._prepare_workspace() and returns: used_offset,  denom,  alphas,  betas,  llForward,  llBackward,  bigblank_durations