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,
      msk_activate="relu",
  ):
      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,
          msk_activate=msk_activate,
      )
      return model
  
imports: from argparse import ArgumentParser
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, TypedDict, Union
  import torch
  import torchaudio
  from pytorch_lightning import LightningModule, Trainer
  from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
  from torch import nn
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils.data import DataLoader
  from utils import metrics
  from utils.dataset import utils
label_desc: Use image node_id 4 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, msk_activate) and returns: model