code: def conv_tasnet_base(num_sources: int = 2) -> ConvTasNet:
      r"""Builds non-causal version of :class:`~torchaudio.models.ConvTasNet`.
  
      The parameter settings follow the ones with the highest Si-SNR metirc score in the paper,
      except the mask activation function is changed from "sigmoid" to "relu" for performance improvement.
  
      Args:
          num_sources (int, optional): Number of sources in the output.
              (Default: 2)
      Returns:
          ConvTasNet:
              ConvTasNet model.
      """
      return ConvTasNet(
          num_sources=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",
      )
  
file_path: audio/src/torchaudio/models/conv_tasnet.py
imports: from typing import Optional, Tuple
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: conv_tasnet_base(num_sources) and returns: ConvTasNet