code: def _get_model(
      num_sources,
      enc_kernel_size=16,
      enc_num_feats=512,
      msk_kernel_size=3,
      msk_num_feats=128,
      msk_num_hidden_feats=512,
      msk_num_layers=8,
      msk_num_stacks=3,
  ):
      model = torchaudio.models.ConvTasNet(
          num_sources=num_sources,
          enc_kernel_size=enc_kernel_size,
          enc_num_feats=enc_num_feats,
          msk_kernel_size=msk_kernel_size,
          msk_num_feats=msk_num_feats,
          msk_num_hidden_feats=msk_num_hidden_feats,
          msk_num_layers=msk_num_layers,
          msk_num_stacks=msk_num_stacks,
      )
      _LG.info_on_master("Model Configuration:")
      _LG.info_on_master(" - N: %d", enc_num_feats)
      _LG.info_on_master(" - L: %d", enc_kernel_size)
      _LG.info_on_master(" - B: %d", msk_num_feats)
      _LG.info_on_master(" - H: %d", msk_num_hidden_feats)
      _LG.info_on_master(" - Sc: %d", msk_num_feats)
      _LG.info_on_master(" - P: %d", msk_kernel_size)
      _LG.info_on_master(" - X: %d", msk_num_layers)
      _LG.info_on_master(" - R: %d", msk_num_stacks)
      _LG.info_on_master(
          " - Receptive Field: %s [samples]",
          model.mask_generator.receptive_field,
      )
      return model
  
file_path: audio/examples/source_separation/conv_tasnet/train.py
imports: import argparse
  import pathlib
  import time
  import conv_tasnet
  import torch
  import torchaudio
  import torchaudio.models
  from utils import dist_utils
  from utils.dataset import utils
label_desc: Use image node_id 2 for calling a global function with example usage: _get_model(num_sources, enc_kernel_size, enc_num_feats, msk_kernel_size, msk_num_feats, msk_num_hidden_feats, msk_num_layers, msk_num_stacks) and returns: model