class_docstr: RPA UL Agent.
  
  Performs unlikelihood such that tokens which lead to misclassification are
  penalized.
class_docstr_tok: ["RPA","UL","Agent",".","Performs","unlikelihood","such","that","tokens","which","lead","to","misclassification","are","penalized","."]
docstr: Evaluate a single batch of examples.
docstr_tok: ["Evaluate","a","single","batch","of","examples","."]
code: def eval_step(self, batch):
      """
      Evaluate a single batch of examples.
      """
      if batch.text_vec is None:
          return
      self.model.eval()
  
      if batch.label_vec is not None:
          # calculate loss on targets with teacher forcing
          loss = self.compute_loss(
              batch
          )  # noqa: F841  we need the side effects
  
      maxlen = self.label_truncate or 256
      beam_preds_scores, _ = self._generate(
          batch, self.beam_size, maxlen
      )
      preds, scores, _ = zip(*beam_preds_scores)
  
      cand_choices = None
      text = (
          [self._v2t(p) for p in preds] if preds is not None else None
      )
      return Output(text, cand_choices)
  
imports: from typing import Optional, List, Tuple
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.torch_agent import Batch
  import torch
  import torch.nn
  import torch.nn.functional
  from parlai.agents.transformer.transformer import TransformerGeneratorAgent
  from parlai.core.torch_agent import Output
  from parlai.core.metrics import AverageMetric
  from projects.light_whoami.agents.rpa_rerank import RPAReranker
  from projects.msc.agents.long_tga import TransformerVariantAgent
label_desc: Use image node_id 7 for calling the RpaUlAgent obj's underlying member method code with example usage: obj.eval_step(batch) and returns: Output