class_docstr: Prediction module that apply pooling and attention, then predict subjective metric scores.
  
  Args:
      input_dim (int): Input feature dimension.
      att_dim (int): Attention Tensor dimension.
class_docstr_tok: ["Prediction","module","that","apply","pooling","and","attention",",","then","predict","subjective","metric","scores",".","Args",":","input_dim","(","int",")",":","Input","feature","dimension",".","att_dim","(","int",")",":","Attention","Tensor","dimension","."]
code: def __init__(self, input_dim: int, att_dim: int):
      super(Predictor, self).__init__()
      self.att_pool_layer = AttPool(input_dim, att_dim)
      self.att_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 Predictor object from inherited base classes: nn with example: obj = Predictor(input_dim, att_dim)