class_docstr: Actual model code for the Transresnet Agent.
docstr: Compute loss - and number of correct examples - for one batch.
  
  :param context_encoded:
      the encoded context
  :param captions_encoded:
      the encoded captions
  :param during_train:
      true if training, else False
  
  :return:
      the batch loss and the number of correct examples
docstr_tok: ["Compute","loss","-","and","number","of","correct","examples","-","for","one","batch",".",":","param","context_encoded",":","the","encoded","context",":","param","captions_encoded",":","the","encoded","captions",":","param","during_train",":","true","if","training",",","else","False",":","return",":","the","batch","loss","and","the","number","of","correct","examples"]
code: def evaluate_one_batch(
      self, context_encoded, captions_encoded, during_train=False
  ):
      """
      Compute loss - and number of correct examples - for one batch.
  
      :param context_encoded:
          the encoded context
      :param captions_encoded:
          the encoded captions
      :param during_train:
          true if training, else False
  
      :return:
          the batch loss and the number of correct examples
      """
      if not during_train:
          self.zero_grad()
          self.eval()
      dot_products = context_encoded.mm(captions_encoded.t())
      log_prob = torch.nn.functional.log_softmax(dot_products, dim=1)
      targets = torch.arange(0, len(context_encoded), dtype=torch.long)
      if self.use_cuda:
          targets = targets.cuda()
      loss = torch.nn.functional.nll_loss(log_prob, targets)
      num_correct = (log_prob.max(dim=1)[1] == targets).float().sum()
      return loss, num_correct
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import torch
  from torch import nn
  from torch import optim
  from parlai.agents.transformer.modules import TransformerEncoder
  from parlai.agents.transformer import transformer
  from parlai.utils.io import PathManager
label_desc: Use image node_id 14 for calling the TransresnetModel obj's underlying member method code with example usage: obj.evaluate_one_batch(context_encoded, captions_encoded, during_train) and returns: loss,  num_correct