code: def gen_hypos(generator, models, num_feats, sample, task, use_cuda):
      sample = utils.move_to_cuda(sample) if use_cuda else sample
  
      if "features" in sample["net_input"]:
          sample["net_input"]["dense_x_only"] = True
          num_feats += (
              sample["net_input"]["features"].shape[0]
              * sample["net_input"]["features"].shape[1]
          )
      hypos = task.inference_step(generator, models, sample, None)
      return hypos, num_feats
  
imports: import ast
  from collections import namedtuple
  from dataclasses import dataclass, field
  from enum import Enum, auto
  import hydra
  from hydra.core.config_store import ConfigStore
  import logging
  import math
  import os
  from omegaconf import OmegaConf
  from typing import Optional
  import sys
  import editdistance
  import torch
  from hydra.core.hydra_config import HydraConfig
  from fairseq import checkpoint_utils, progress_bar, tasks, utils
  from fairseq.data.data_utils import post_process
  from fairseq.dataclass.configs import FairseqDataclass, FairseqConfig
  from fairseq.logging.meters import StopwatchMeter
  from omegaconf import open_dict
  from examples.speech_recognition.kaldi.kaldi_decoder import KaldiDecoderConfig
label_desc: Use image node_id 6 for calling a global function with example usage: gen_hypos(generator, models, num_feats, sample, task, use_cuda) and returns: hypos,  num_feats