docstr: Given an input batch it computes the output probabilities.
code: def compute_forward(self, batch, stage):
      "Given an input batch it computes the output probabilities."
      batch = batch.to(self.device)
      wavs, lens = batch.sig
      feats = self.modules.compute_features(wavs)
      feats = self.modules.mean_var_norm(feats, lens)
  
      # Transcription network: input-output dependency
      TN_output = self.modules.enc(feats)
      TN_output = self.modules.enc_lin(TN_output)
  
      # Prediction network: output-output dependency
      targets, target_lens = batch.phn_encoded_bos
      PN_output = self.modules.emb(targets)
      PN_output, _ = self.modules.dec(PN_output)
      PN_output = self.modules.dec_lin(PN_output)
  
      # Joint the networks
      joint = self.modules.Tjoint(
          TN_output.unsqueeze(2),
          PN_output.unsqueeze(1),
      )
      outputs = self.modules.output(joint)
      outputs = self.hparams.log_softmax(outputs)
      if stage == sb.Stage.TRAIN:
          return outputs, lens
      else:
          hyps, _, _, _ = self.hparams.searcher(TN_output)
          return outputs, lens, hyps
  
file_path: speechbrain/tests/integration/ASR_Transducer/example_asr_transducer_experiment.py
imports: import pathlib
  import speechbrain
  from hyperpyyaml import load_hyperpyyaml
label_desc: Use image node_id 1 for calling the TransducerBrain obj's underlying member method code with example usage: obj.compute_forward(batch, stage) and returns: outputs,  lens, outputs,  lens,  hyps