class_docstr: Speech Quality and Intelligibility Measures (SQUIM) model that predicts **subjective** metric scores
  for speech enhancement (e.g., Mean Opinion Score (MOS)). The model is adopted from *NORESQA-MOS*
  :cite:`manocha2022speech` which predicts MOS scores given the input speech and a non-matching reference.
  
  Args:
      ssl_model (torch.nn.Module): The self-supervised learning model for feature extraction.
      projector (torch.nn.Module): Projection layer that projects SSL feature to a lower dimension.
      predictor (torch.nn.Module): Predict the subjective scores.
class_docstr_tok: ["Speech","Quality","and","Intelligibility","Measures","(","SQUIM",")","model","that","predicts","*","*","subjective","*","*","metric","scores","for","speech","enhancement","(","e.g.",",","Mean","Opinion","Score","(","MOS",")",")",".","The","model","is","adopted","from","*","NORESQA-MOS","*",":","cite",":","`","manocha2022speech","`","which","predicts","MOS","scores","given","the","input","speech","and","a","non-matching","reference",".","Args",":","ssl_model","(","torch.nn.Module",")",":","The","self-supervised","learning","model","for","feature","extraction",".","projector","(","torch.nn.Module",")",":","Projection","layer","that","projects","SSL","feature","to","a","lower","dimension",".","predictor","(","torch.nn.Module",")",":","Predict","the","subjective","scores","."]
code: def __init__(
      self,
      ssl_model: nn.Module,
      projector: nn.Module,
      predictor: nn.Module,
  ):
      super(SquimSubjective, self).__init__()
      self.ssl_model = ssl_model
      self.projector = projector
      self.predictor = 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 to create a new SquimSubjective object from inherited base classes: nn with example: obj = SquimSubjective(ssl_model, projector, predictor)