docstr: Generate a batch of inferences.
code: def generate(self, models, sample, **unused):
      """Generate a batch of inferences."""
      # model.forward normally channels prev_output_tokens into the decoder
      # separately, but SequenceGenerator directly calls model.encoder
      encoder_input = {
          k: v
          for k, v in sample["net_input"].items()
          if k != "prev_output_tokens"
      }
      emissions = self.get_emissions(models, encoder_input)
      return self.decode(emissions)
  
imports: import gc
  import itertools
  import os.path
  from typing import List
  import warnings
  from collections import deque, namedtuple
  import numpy
  import torch
  from examples.speech_recognition.data.replabels import unpack_replabels
  from fairseq import tasks
  from fairseq.utils import apply_to_sample
  from omegaconf import open_dict
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
label_desc: Use image node_id 2 for calling the W2lDecoder obj's underlying member method code with example usage: obj.generate(models, sample) and returns: self