code: def get_funcs_to_unary_input_classifier(
      cls,
      call_func: str,
      source: str,
      save_dir: str,
      device: torch.device,
      example_audio=None,
      batch_label="wavs",
      lengths_label: Optional[str] = "wav_lens",
  ):
      """Implement get_funcs_to_unary_input_classifier."""
      assert issubclass(cls, Pretrained)
      pretrained = cls.from_hparams(
          source=source, savedir=save_dir, run_opts={"device": device}
      )
      example = (
          pretrained.load_audio(example_audio)
          if example_audio
          else None
      )
  
      def prepare(batch_size, duration, sampling_rate=16000):
          """Prepares input data."""
          unary_input = {
              batch_label: example[: duration * sampling_rate].repeat(
                  batch_size, 1
              )
              if example is not None
              else torch.rand(
                  (batch_size, duration * sampling_rate), device=device
              ),
          }
          if lengths_label is not None:
              unary_input[lengths_label] = torch.ones(batch_size)
          return unary_input
  
      def call(model, **kwargs):
          """Calls the specified funnction."""
          getattr(model, call_func)(**kwargs)
  
      return prepare, call, pretrained
  
file_path: speechbrain/tools/profiling/profile.py
imports: import sys
  import torch
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.profiling import profile_report, export, report_time, report_memory
  from speechbrain.inference import Pretrained, EncoderDecoderASR, EncoderASR, EndToEndSLU, EncoderClassifier, SpeakerRecognition, VAD, SepformerSeparation, SpectralMaskEnhancement, SNREstimator
  from typing import Optional, List
label_desc: Use image node_id 1 for calling a global function with example usage: get_funcs_to_unary_input_classifier(cls, call_func, source, save_dir, device, example_audio, batch_label, lengths_label) and returns: prepare,  call,  pretrained, unary_input