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)
      wavs, lens = batch.sig
  
      # Feature extraction and normalization
      feats = self.modules.compute_features(wavs)
      feats = self.modules.mean_var_norm(feats, lens)
  
      # Embeddings + speaker classifier
      embeddings = self.modules.embedding_model(feats, lens)
      outputs = self.modules.classifier(embeddings)
  
      return outputs
  
file_path: speechbrain/recipes/IEMOCAP/emotion_recognition/train.py
imports: import os
  import sys
  import csv
  import speechbrain
  import torch
  from torch.utils.data import DataLoader
  from enum import Enum, auto
  from tqdm.contrib import tqdm
  from hyperpyyaml import load_hyperpyyaml
label_desc: Use image node_id 1 for calling the EmoIdBrain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: outputs