code: def squim_subjective_model(
      ssl_type: str,
      feat_dim: int,
      proj_dim: int,
      att_dim: int,
  ) -> SquimSubjective:
      """Build a custome :class:`torchaudio.prototype.models.SquimSubjective` model.
  
      Args:
          ssl_type (str): Type of self-supervised learning (SSL) models.
              Must be one of ["wav2vec2_base", "wav2vec2_large"].
          feat_dim (int): Feature dimension of the SSL feature representation.
          proj_dim (int): Output dimension of projection layer.
          att_dim (int): Dimension of attention scores.
      """
      ssl_model = getattr(torchaudio.models, ssl_type)()
      projector = nn.Linear(feat_dim, proj_dim)
      predictor = Predictor(proj_dim * 2, att_dim)
      return SquimSubjective(ssl_model, projector, predictor)
  
file_path: audio/src/torchaudio/models/squim/subjective.py
imports: from typing import Tuple
  import torch
  import torch.nn
  import torchaudio
label_desc: Use image node_id 1 for calling a global function with example usage: squim_subjective_model(ssl_type, feat_dim, proj_dim, att_dim) and returns: SquimSubjective