class_docstr: Dataclass that bundles components for performing source separation.
  
  Example
      >>> import torchaudio
      >>> from torchaudio.pipelines import CONVTASNET_BASE_LIBRI2MIX
      >>> import torch
      >>>
      >>> # Build the separation model.
      >>> model = CONVTASNET_BASE_LIBRI2MIX.get_model()
      >>> 100%|███████████████████████████████|19.1M/19.1M [00:04<00:00, 4.93MB/s]
      >>>
      >>> # Instantiate the test set of Libri2Mix dataset.
      >>> dataset = torchaudio.datasets.LibriMix("/home/datasets/", subset="test")
      >>>
      >>> # Apply source separation on mixture audio.
      >>> for i, data in enumerate(dataset):
      >>>     sample_rate, mixture, clean_sources = data
      >>>     # Make sure the shape of input suits the model requirement.
      >>>     mixture = mixture.reshape(1, 1, -1)
      >>>     estimated_sources = model(mixture)
      >>>     score = si_snr_pit(estimated_sources, clean_sources) # for demonstration
      >>>     print(f"Si-SNR score is : {score}.)
      >>>     break
      >>> Si-SNR score is : 16.24.
      >>>
docstr: Construct the model and load the pretrained weight.
code: def get_model(self) -> torch.nn.Module:
      """Construct the model and load the pretrained weight."""
      model = self._model_factory_func()
      path = torchaudio.utils.download_asset(self._model_path)
      state_dict = torch.load(path)
      model.load_state_dict(state_dict)
      model.eval()
      return model
  
file_path: audio/src/torchaudio/pipelines/_source_separation_pipeline.py
imports: from dataclasses import dataclass
  from functools import partial
  from typing import Callable
  import torch
  import torchaudio
  from torchaudio.models import conv_tasnet_base, hdemucs_high
label_desc: Use image node_id 2 for calling the SourceSeparationBundle obj's underlying member method code with example usage: obj.get_model() and returns: model