class_docstr: Poly-encoder Agent.
  
  Equivalent of bert_ranker/polyencoder and biencoder_multiple_output but does not
  rely on an external library (hugging face).
class_docstr_tok: ["Poly-encoder","Agent",".","Equivalent","of","bert_ranker\/polyencoder","and","biencoder_multiple_output","but","does","not","rely","on","an","external","library","(","hugging","face",")","."]
docstr: Return the size of the batch.
  
  Can be overridden by subclasses that do not always have text input.
docstr_tok: ["Return","the","size","of","the","batch",".","Can","be","overridden","by","subclasses","that","do","not","always","have","text","input","."]
code: def _get_batch_size(self, batch) -> int:
      """
      Return the size of the batch.
  
      Can be overridden by subclasses that do not always have text input.
      """
      return batch.text_vec.size(0)
  
imports: from parlai.core.params import ParlaiParser
  from typing import Any, Dict, Optional, Tuple
  import torch
  from parlai.core.opt import Opt
  from parlai.core.torch_ranker_agent import TorchRankerAgent
  from parlai.core.torch_agent import Batch
  from parlai.utils.misc import recursive_getattr
  from parlai.utils.logging import logging
  from .biencoder import AddLabelFixedCandsTRA
  from .modules import BasicAttention, MultiHeadAttention, TransformerEncoder
  from .transformer import TransformerRankerAgent
label_desc: Use image node_id 14 for calling the PolyencoderAgent obj's underlying member method code with example usage: obj._get_batch_size(batch) and returns: batch