code: def get_features_infer(
      queries: List[str],
      tokenizer: TokenizerSpec,
      max_seq_length: int = 64,
      step: Optional[int] = 8,
      margin: Optional[int] = 16,
      audio_queries: Optional[Union[List[bytes], List[str]]] = None,
      target_sr: Optional[int] = None,
  ) -> Tuple[
      List[List[int]],
      List[List[int]],
      List[List[int]],
      List[List[int]],
      List[int],
      List[int],
      List[bool],
      List[bool],
      Optional[List[float]],
      Optional[List[int]],
  ]:
      """
      Processes the data and returns features.
  
      Args:
          queries: text sequences
          tokenizer: such as AutoTokenizer
          max_seq_length: max sequence length minus 2 for [CLS] and [SEP]
          step: relative shift of consequent segments into which long queries are split. Long queries are split into
              segments which can overlap. Parameter ``step`` controls such overlapping. Imagine that queries are
              tokenized into characters, ``max_seq_length=5``, and ``step=2``. In such a case query "hello" is
              tokenized into segments ``[['[CLS]', 'h', 'e', 'l', '[SEP]'], ['[CLS]', 'l', 'l', 'o', '[SEP]']]``.
          margin: number of subtokens near edges of segments which are not used for punctuation and capitalization
              prediction. The first segment does not have left margin and the last segment does not have right
              margin. For example, if input sequence is tokenized into characters, ``max_seq_length=5``,
              ``step=1``, and ``margin=1``, then query "hello" will be tokenized into segments
              ``[['[CLS]', 'h', 'e', 'l', '[SEP]'], ['[CLS]', 'e', 'l', 'l', '[SEP]'],
              ['[CLS]', 'l', 'l', 'o', '[SEP]']]``. These segments are passed to the model. Before final predictions
              computation, margins are removed. In the next list, subtokens which logits are not used for final
              predictions computation are marked with asterisk: ``[['[CLS]'*, 'h', 'e', 'l'*, '[SEP]'*],
              ['[CLS]'*, 'e'*, 'l', 'l'*, '[SEP]'*], ['[CLS]'*, 'l'*, 'l', 'o', '[SEP]'*]]``.
          audio_queries (:obj:`List[str]`, `optional`): paths to audio files.
          target_sr (:obj:`int`, `optional`): target sample rate for audios.
  
      Returns:
          all_input_ids: list of input ids of all segments
          all_segment_ids: token type ids of all segments
          all_input_mask: attention mask to use for BERT model
          all_subtokens_mask: masks out all subwords besides the first one
          all_quantities_of_preceding_words: number of words in query preceding a segment. Used for joining
              predictions from overlapping segments.
          all_query_ids: index of a query to which segment belongs
          all_is_first: is segment first segment in a query
          all_is_last: is segment last segment in a query
      """
      st = []
      stm = []
      sent_lengths = []
      audios = []
      audio_queries = (
          audio_queries if audio_queries else [None] * len(queries)
      )  # Dummy if no `audio_queries` passed
      for i, (query, audio_query) in enumerate(
          zip(queries, audio_queries)
      ):
          subtokens, subtokens_mask = _get_subtokens_and_subtokens_mask(
              query, tokenizer
          )
          sent_lengths.append(len(subtokens))
          st.append(subtokens)
          stm.append(subtokens_mask)
          if audio_query:
              if ASR_AVAILABLE:
                  if isinstance(audio_query, bytes):
                      audios.append(
                          AudioSegment.from_file(
                              io.BytesIO(audio_query),
                              target_sr=target_sr,
                          )
                      )
                  elif isinstance(audio_query, str):
                      audios.append(
                          AudioSegment.from_file(
                              audio_query.strip(), target_sr=target_sr
                          )
                      )
              else:
                  raise ModuleNotFoundError(
                      "Nemo ASR was not installed, see https://github.com/NVIDIA/NeMo#installation for installation instructions"
                  )
      audios = audios if len(audios) else [None] * len(st)
      _check_max_seq_length_and_margin_and_step(
          max_seq_length, margin, step
      )
      if max_seq_length > max(sent_lengths) + 2:
          max_seq_length = max(sent_lengths) + 2
          # If `max_seq_length` is greater than maximum length of input query, parameters ``margin`` and ``step`` are
          # not used will not be used.
          step = 1
          # Maximum number of word subtokens in segment. The first and the last tokens in segment are CLS and EOS
          length = max_seq_length - 2
      else:
          # Maximum number of word subtokens in segment. The first and the last tokens in segment are CLS and EOS
          length = max_seq_length - 2
          step = min(length - margin * 2, step)
      logging.info(f"Max length: {max_seq_length}")
      get_stats(sent_lengths)
      (
          all_input_ids,
          all_segment_ids,
          all_subtokens_mask,
          all_input_mask,
          all_input_mask,
      ) = ([], [], [], [], [])
      (
          all_quantities_of_preceding_words,
          all_query_ids,
          all_is_first,
          all_is_last,
      ) = ([], [], [], [])
      all_audio_queries, all_audio_lengths = [], []
      for q_i, (query_st, query_audio) in enumerate(zip(st, audios)):
          (
              q_inp_ids,
              q_segment_ids,
              q_subtokens_mask,
              q_inp_mask,
              q_quantities_of_preceding_words,
          ) = ([], [], [], [], [])
          q_audio_queries, q_audio_lengths = [], []
          if query_audio and length < len(query_st):
              logging.info(f"Ignoring query with id {q_i}")
              continue
          for i in range(
              0, max(len(query_st), length) - length + step, step
          ):
              subtokens = (
                  [tokenizer.cls_token]
                  + query_st[i : i + length]
                  + [tokenizer.sep_token]
              )
              q_inp_ids.append(tokenizer.tokens_to_ids(subtokens))
              q_segment_ids.append([0] * len(subtokens))
              q_subtokens_mask.append(
                  [False] + stm[q_i][i : i + length] + [False]
              )
              q_inp_mask.append([True] * len(subtokens))
              q_quantities_of_preceding_words.append(
                  np.count_nonzero(stm[q_i][:i])
              )
              if query_audio:
                  samples = query_audio.samples
                  q_audio_queries.append(samples)
                  q_audio_lengths.append(len(samples))
          all_input_ids.append(q_inp_ids)
          all_segment_ids.append(q_segment_ids)
          all_subtokens_mask.append(q_subtokens_mask)
          all_input_mask.append(q_inp_mask)
          all_quantities_of_preceding_words.append(
              q_quantities_of_preceding_words
          )
          all_query_ids.append([q_i] * len(q_inp_ids))
          all_is_first.append([True] + [False] * (len(q_inp_ids) - 1))
          all_is_last.append([False] * (len(q_inp_ids) - 1) + [True])
          if query_audio:
              all_audio_queries.append(q_audio_queries)
              all_audio_lengths.append(q_audio_lengths)
      return (
          list(itertools.chain(*all_input_ids)),
          list(itertools.chain(*all_segment_ids)),
          list(itertools.chain(*all_input_mask)),
          list(itertools.chain(*all_subtokens_mask)),
          list(itertools.chain(*all_quantities_of_preceding_words)),
          list(itertools.chain(*all_query_ids)),
          list(itertools.chain(*all_is_first)),
          list(itertools.chain(*all_is_last)),
          list(itertools.chain(*all_audio_queries)),
          list(itertools.chain(*all_audio_lengths)),
      )
  
imports: import io
  import itertools
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from numpy import ndarray
  from torch import Tensor
  from torch.nn.utils.rnn import pad_sequence
  from nemo.collections.common.tokenizers import TokenizerSpec
  from nemo.collections.nlp.data import get_stats
  from nemo.core import Dataset
  from nemo.core.neural_types import ChannelType, Index, MaskType, NeuralType
  from nemo.core.neural_types.elements import AudioSignal, BoolType, LengthsType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: get_features_infer(queries, tokenizer, max_seq_length, step, margin, audio_queries, target_sr) and returns: list,  list,  list,  list,  list,  list,  list,  list,  list,  list