class_docstr: Attention-Pooling module that estimates the attention score.
  
  Args:
      input_dim (int): Input feature dimension.
      att_dim (int): Attention Tensor dimension.
class_docstr_tok: ["Attention-Pooling","module","that","estimates","the","attention","score",".","Args",":","input_dim","(","int",")",":","Input","feature","dimension",".","att_dim","(","int",")",":","Attention","Tensor","dimension","."]
code: def __init__(self, input_dim: int, att_dim: int):
      super(AttPool, self).__init__()
  
      self.linear1 = nn.Linear(input_dim, 1)
      self.linear2 = nn.Linear(input_dim, att_dim)
  
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 AttPool object from inherited base classes: nn with example: obj = AttPool(input_dim, att_dim)