class_docstr: Model described in (https://arxiv.org/abs/1811.00945).
  
  A model for producing engaging captions about an image. Given an image and
  this model will attempt to predict an appropriate
  next utterance in the dialog, in the context of a given personality.
  
  See the paper linked above for more information.
class_docstr_tok: ["Model","described","in","(","https",":","\/\/arxiv.org\/abs\/1811.00945",")",".","A","model","for","producing","engaging","captions","about","an","image",".","Given","an","image","and","this","model","will","attempt","to","predict","an","appropriate","next","utterance","in","the","dialog",",","in","the","context","of","a","given","personality",".","See","the","paper","linked","above","for","more","information","."]
docstr: Model train step.
  
  :param valid_obs:
      list of valid observations
  
  :param image_feats:
      list of image features, one per example
  
  :param personalities:
      list of personalities, one per example
  
  :return:
      the total loss, number of correct examples, and total number of
      examples evaluated
docstr_tok: ["Model","train","step",".",":","param","valid_obs",":","list","of","valid","observations",":","param","image_feats",":","list","of","image","features",",","one","per","example",":","param","personalities",":","list","of","personalities",",","one","per","example",":","return",":","the","total","loss",",","number","of","correct","examples",",","and","total","number","of","examples","evaluated"]
code: def train_step(self, valid_obs, image_feats, personalities):
      """
      Model train step.
  
      :param valid_obs:
          list of valid observations
  
      :param image_feats:
          list of image features, one per example
  
      :param personalities:
          list of personalities, one per example
  
      :return:
          the total loss, number of correct examples, and total number of
          examples evaluated
      """
      comments = [random.choice(v["labels"]) for v in valid_obs]
      loss, num_correct, num_examples = self.model.train_batch(
          image_feats, personalities, comments
      )
      return loss, num_correct, num_examples
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.agents import Agent
  from parlai.core.dict import DictionaryAgent
  from parlai.utils.misc import round_sigfigs
  from .modules import TransresnetModel
  from parlai.tasks.personality_captions.build import build
  from parlai.utils.io import PathManager
  import parlai.utils.torch
  import os
  import random
  import json
  import numpy
  import torch
  import tqdm
label_desc: Use image node_id 10 for calling the TransresnetAgent obj's underlying member method code with example usage: obj.train_step(valid_obs, image_feats, personalities) and returns: loss,  num_correct,  num_examples