class_docstr: Speech Quality and Intelligibility Measures (SQUIM) model that predicts **objective** metric scores
  for speech enhancement (e.g., STOI, PESQ, and SI-SDR).
  
  Args:
      encoder (torch.nn.Module): Encoder module to transform 1D waveform to 2D feature representation.
      dprnn (torch.nn.Module): DPRNN module to model sequential feature.
      branches (torch.nn.ModuleList): Transformer branches in which each branch estimate one objective metirc score.
code: def __init__(
      self,
      encoder: nn.Module,
      dprnn: nn.Module,
      branches: nn.ModuleList,
  ):
      super(SquimObjective, self).__init__()
      self.encoder = encoder
      self.dprnn = dprnn
      self.branches = branches
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new SquimObjective object from inherited base classes: nn with example: obj = SquimObjective(encoder, dprnn, branches)