class_docstr: This constructs a simple bag of words model.
  
  It contains a encoder for encoding candidates and context.
code: def forward(self, batch, cand_vecs, cand_encs=None):
      bsz = cand_vecs.size(0)
      if cand_encs is None:
          if cand_vecs.dim() == 3:
              # if dim = 3, bsz *  num_candidates * seq_length
              # In this case, we are using inline candidates
              cand_vecs = cand_vecs.reshape(-1, cand_vecs.size(2))
          cand_encs = self.encode_text(cand_vecs)
          if cand_encs.size(0) != bsz:
              # Some cases, we could also use batch candidates,
              # they will be size of bsz * seq_length
              # so we don't have to use the extra treatment
              cand_encs = cand_encs.reshape(bsz, -1, self.hidden_dim)
      context_encodings = self.encode_text(batch.text_vec)
      if context_encodings.dim() != cand_encs.dim():
          return torch.sum(
              context_encodings.unsqueeze(1).expand_as(cand_encs)
              * cand_encs,
              2,
          )
      return context_encodings.mm(cand_encs.t())
  
file_path: ParlAI/parlai/agents/examples/tra.py
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  import torch
  from torch import nn
label_desc: Use image node_id 3 for calling the ExampleBagOfWordsModel obj's underlying member method code with example usage: obj.forward(batch, cand_vecs, cand_encs) and returns: context_encodings, torch