class_docstr: Actual model code for the Transresnet Agent.
docstr: Evaluate a batch of 100 examples.
  
  The captions of the other examples are used as negatives.
  
  :param context_encoded:
      the encoded context
  :param captions_encoded:
      the encoded captions
  
  :return:
      the total loss, the total number of correct examples, and the
      total number of examples evaluated.
docstr_tok: ["Evaluate","a","batch","of","100","examples",".","The","captions","of","the","other","examples","are","used","as","negatives",".",":","param","context_encoded",":","the","encoded","context",":","param","captions_encoded",":","the","encoded","captions",":","return",":","the","total","loss",",","the","total","number","of","correct","examples",",","and","the","total","number","of","examples","evaluated","."]
code: def eval_batch_of_100(self, context_encoded, captions_encoded):
      """
      Evaluate a batch of 100 examples.
  
      The captions of the other examples are used as negatives.
  
      :param context_encoded:
          the encoded context
      :param captions_encoded:
          the encoded captions
  
      :return:
          the total loss, the total number of correct examples, and the
          total number of examples evaluated.
      """
      total_loss = 0
      total_ok = 0
      num_examples = 0
      for i in range(0, len(context_encoded), 100):
          if i + 100 > len(context_encoded):
              break
          num_examples += 100
          loss, num_correct = self.evaluate_one_batch(
              context_encoded[i : i + 100, :],
              captions_encoded[i : i + 100, :],
          )
          total_loss += loss.data.cpu().item()
          total_ok += num_correct.data.cpu().item()
      return total_loss, total_ok, num_examples
  
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 13 for calling the TransresnetModel obj's underlying member method code with example usage: obj.eval_batch_of_100(context_encoded, captions_encoded) and returns: total_loss,  total_ok,  num_examples