docstr: Computation pipeline based on a encoder + emotion classifier.
          
docstr_tok: ["Computation","pipeline","based","on","a","encoder","+","emotion","classifier","."]
code: def compute_forward(self, batch, stage):
      """Computation pipeline based on a encoder + emotion classifier."""
      batch = batch.to(self.device)
  
      self.modules = self.modules.to(self.device)
  
      wavs, lens = batch.sig
      wavs = self.hparams.input_norm(wavs, lens)
      outputs = self.modules.wav2vec2(wavs)
      averaged_out = self.hparams.avg_pool(outputs)
  
      outputs = self.modules.output_mlp(averaged_out)
  
      outputs = self.hparams.log_softmax(outputs)
  
      return outputs
  
file_path: speechbrain/recipes/ZaionEmotionDataset/emotion_diarization/train.py
imports: import os
  import sys
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
  import torch
  import json
  import itertools
  from speechbrain.utils.EDER import EDER
label_desc: Use image node_id 1 for calling the EmoDiaBrain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: outputs