class_docstr: Class to store batched hypotheses (labels, time_indices, scores) for efficient RNNT decoding
class_docstr_tok: ["Class","to","store","batched","hypotheses","(","labels",",","time_indices",",","scores",")","for","efficient","RNNT","decoding"]
docstr: Allocate 2x space for tensors, similar to common C++ std::vector implementations
  to maintain O(1) insertion time complexity
docstr_tok: ["Allocate","2x","space","for","tensors",",","similar","to","common","C++","std",":",":vector","implementations","to","maintain","O","(","1",")","insertion","time","complexity"]
code: def _allocate_more(self):
      """
      Allocate 2x space for tensors, similar to common C++ std::vector implementations
      to maintain O(1) insertion time complexity
      """
      self.transcript = torch.cat(
          (self.transcript, torch.zeros_like(self.transcript)), dim=-1
      )
      self.timesteps = torch.cat(
          (self.timesteps, torch.zeros_like(self.timesteps)), dim=-1
      )
      self._max_length *= 2
  
file_path: NeMo/nemo/collections/asr/parts/utils/rnnt_utils.py
imports: from dataclasses import dataclass, field
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
label_desc: Use image node_id 2 for calling the BatchedHyps obj's underlying member method code with example usage: obj._allocate_more() without return types