code: def get_emissions(
      self,
      models: List[FairseqModel],
      encoder_input: Dict[str, Any],
  ) -> torch.FloatTensor:
      model = models[0]
      encoder_out = model(**encoder_input)
      if hasattr(model, "get_logits"):
          emissions = model.get_logits(encoder_out)
      else:
          emissions = model.get_normalized_probs(
              encoder_out, log_probs=True
          )
      return emissions.transpose(0, 1).float().cpu().contiguous()
  
file_path: fairseq/examples/speech_recognition/new/decoders/base_decoder.py
imports: import itertools
  from typing import Any, Dict, List
  import torch
  from fairseq.data.dictionary import Dictionary
  from fairseq.models.fairseq_model import FairseqModel
label_desc: Use image node_id 3 for calling the BaseDecoder obj's underlying member method code with example usage: obj.get_emissions(models, encoder_input) and returns: emissions