class_docstr: Model for end-to-end speech intent classification and slot filling, which is formulated as a speech-to-sequence task
class_docstr_tok: ["Model","for","end-to-end","speech","intent","classification","and","slot","filling",",","which","is","formulated","as","a","speech-to-sequence","task"]
code: def predict(
      self,
      input_signal,
      input_signal_length,
      processed_signal=None,
      processed_signal_length=None,
      dataloader_idx=0,
  ) -> List[str]:
      has_input_signal = (
          input_signal is not None and input_signal_length is not None
      )
      has_processed_signal = (
          processed_signal is not None
          and processed_signal_length is not None
      )
      if (has_input_signal ^ has_processed_signal) == False:
          raise ValueError(
              f"{self} Arguments ``input_signal`` and ``input_signal_length`` are mutually exclusive "
              " with ``processed_signal`` and ``processed_signal_len`` arguments."
          )
  
      if not has_processed_signal:
          processed_signal, processed_signal_length = self.preprocessor(
              input_signal=input_signal,
              length=input_signal_length,
          )
  
      if self.spec_augmentation is not None and self.training:
          processed_signal = self.spec_augmentation(
              input_spec=processed_signal,
              length=processed_signal_length,
          )
  
      encoded, encoded_len = self.encoder(
          audio_signal=processed_signal, length=processed_signal_length
      )
      encoded = encoded.transpose(1, 2)  # BxDxT -> BxTxD
      encoded_mask = get_seq_mask(encoded, encoded_len)
  
      pred_tokens = self.sequence_generator(encoded, encoded_mask)
      predictions = (
          self.sequence_generator.decode_semantics_from_tokens(
              pred_tokens
          )
      )
      return predictions
  
imports: import json
  import os
  import tempfile
  from math import ceil
  from typing import Dict, List, Optional, Union
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  from tqdm.auto import tqdm
  from nemo.collections.asr.data import audio_to_text_dataset
  from nemo.collections.asr.data.audio_to_text_dali import DALIOutputs
  from nemo.collections.asr.metrics.wer import WER
  from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel
  from nemo.collections.asr.parts.mixins import ASRBPEMixin, ASRModuleMixin
  from nemo.collections.asr.parts.preprocessing.perturb import process_augmentations
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig
  from nemo.collections.asr.parts.utils.slu_utils import SequenceGenerator, SequenceGeneratorConfig, get_seq_mask
  from nemo.collections.common.losses import SmoothedNLLLoss
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types import AudioSignal, LabelsType, LengthsType, LogprobsType, NeuralType, SpectrogramType
  from nemo.utils import logging, model_utils
label_desc: Use image node_id 7 for calling the SLUIntentSlotBPEModel obj's underlying member method code with example usage: obj.predict(input_signal, input_signal_length, processed_signal, processed_signal_length, dataloader_idx) and returns: predictions