code: def pack_hypotheses(hypotheses: List[Hypothesis]) -> List[Hypothesis]:
      for idx, hyp in enumerate(
          hypotheses
      ):  # type: rnnt_utils.Hypothesis
          hyp.y_sequence = torch.tensor(
              hyp.y_sequence, dtype=torch.long
          )
  
          if hyp.dec_state is not None:
              hyp.dec_state = _states_to_device(hyp.dec_state)
  
          # Remove -1 from timestep
          if (
              hyp.timestep is not None
              and len(hyp.timestep) > 0
              and hyp.timestep[0] == -1
          ):
              hyp.timestep = hyp.timestep[1:]
  
      return hypotheses
  
imports: import copy
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from tqdm import tqdm
  from nemo.collections.asr.modules import rnnt_abstract
  from nemo.collections.asr.parts.utils.rnnt_utils import HATJointOutput, Hypothesis, NBestHypotheses, is_prefix, select_k_expansions
  from nemo.core.classes import Typing, typecheck
  from nemo.core.neural_types import AcousticEncodedRepresentation, HypothesisType, LengthsType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: pack_hypotheses(hypotheses) and returns: hypotheses