class_docstr: Simple implementation of the starspace algorithm: https://arxiv.org/abs/1709.03856.
docstr: Produce a prediction from our model.
  
  Update the model using the targets if available, otherwise rank candidates as
  well if they are available and param is set.
docstr_tok: ["Produce","a","prediction","from","our","model",".","Update","the","model","using","the","targets","if","available",",","otherwise","rank","candidates","as","well","if","they","are","available","and","param","is","set","."]
code: def predict(self, xs, ys=None, cands=None, cands_txt=None, obs=None):
      """
      Produce a prediction from our model.
  
      Update the model using the targets if available, otherwise rank candidates as
      well if they are available and param is set.
      """
      is_training = ys is not None
      if is_training:
          negs = self.get_negs(xs, ys)
          if is_training and len(negs) > 0:
              self.model.train()
              self.optimizer.zero_grad()
              if self.opt.get("input_dropout", 0) > 0:
                  xs, ys, negs = self.input_dropout(xs, ys, negs)
              xe, ye = self.model(xs, ys, negs)
              y = -(torch.ones(xe.size(0)))
              y[0] = 1
              loss = self.criterion(xe, ye, y)
              loss.backward()
              self.optimizer.step()
              pred = nn.CosineSimilarity().forward(xe, ye)
              metrics = self.compute_metrics(
                  loss.item(), pred.data.squeeze()
              )
              return [{"metrics": metrics}]
      else:
          self.model.eval()
          if cands is None or cands[0] is None:
              # cannot predict without candidates.
              if self.fixedCands:
                  cands = [self.fixedCands]
                  cands_txt = [self.fixedCands_txt]
              else:
                  return [{"text": "I dunno."}]
              # test set prediction uses fixed candidates
              if self.fixedX is None:
                  xe, ye = self.model(xs, ys, self.fixedCands)
                  self.fixedX = ye
              else:
                  # fixed candidate embed vectors are cached, don't recompute
                  blah = torch.LongTensor([1])
                  xe, ye = self.model(xs, ys, [blah])
                  ye = self.fixedX
          else:
              # test set prediction uses candidates
              xe, ye = self.model(xs, ys, cands[0])
          pred = nn.CosineSimilarity().forward(xe, ye)
          # This is somewhat costly which we could avoid if we do not evaluate ranking.
          # i.e. by only doing: val,ind = pred.max(0)
          val, ind = pred.sort(descending=True)
          # predict the highest scoring candidate, and return it.
          ypred = cands_txt[0][ind.data[0]]
          tc = []
          for i in range(min(100, ind.size(0))):
              tc.append(cands_txt[0][ind.data[i]])
          ret = [{"text": ypred, "text_candidates": tc}]
          return ret
      return [{"id": self.getID()}]
  
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 maintain_dialog_history, load_cands
  from parlai.core.torch_agent import TorchAgent
  from parlai.utils.io import PathManager
  import parlai.utils.torch
  import parlai.utils.logging
  from .modules import Starspace
  import torch
  from torch import optim
  import torch.nn
  from collections import deque
  import copy
  import random
  import json
label_desc: Use image node_id 15 for calling the StarspaceAgent obj's underlying member method code with example usage: obj.predict(xs, ys, cands, cands_txt, obs) and returns: list, ret, list, list