class_docstr: Implements a 'n-tower' MLP: one for each of the multi labels
  Used in USM/EA: the user satisfaction modeling, pTSR prediction and
  Error Attribution are all 3 label sets that need predicting.
class_docstr_tok: ["Implements","a","'n-tower","'","MLP",":","one","for","each","of","the","multi","labels","Used","in","USM\/EA",":","the","user","satisfaction","modeling",",","pTSR","prediction","and","Error","Attribution","are","all","3","label","sets","that","need","predicting","."]
code: def forward(self, *input: torch.Tensor):
      logits = tuple(
          self.label_mlps[x](torch.cat(input, 1))
          for x in self.label_names
      )
      return logits
  
file_path: pytext/pytext/models/decoders/multilabel_decoder.py
imports: from typing import Dict, List
  import torch
  import torch.nn
  from pytext.utils.usage import log_class_usage
  from .decoder_base import DecoderBase
label_desc: Use image node_id 3 for calling the MultiLabelDecoder obj's underlying member method code with example usage: obj.forward() and returns: logits