class_docstr: A base classification model that scores a pair of texts.
  
  Subclasses need to implement the from_config, forward and save_modules.
class_docstr_tok: ["A","base","classification","model","that","scores","a","pair","of","texts",".","Subclasses","need","to","implement","the","from_config",",","forward","and","save_modules","."]
code: def _encode_relations(
      cls, encodings: List[torch.Tensor]
  ) -> List[torch.Tensor]:
      for rep_l, rep_r in itertools.combinations(encodings, 2):
          encodings.append(torch.abs(rep_l - rep_r))
          encodings.append(rep_l * rep_r)
      return encodings
  
imports: import itertools
  import os
  from typing import Dict, List, Tuple, Union
  import torch
  import torch.nn
  from pytext.data.tensorizers import LabelTensorizer, Tensorizer, TokenTensorizer
  from pytext.models.decoders import DecoderBase
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.embeddings import EmbeddingBase, EmbeddingList, WordEmbedding
  from pytext.models.model import BaseModel
  from pytext.models.module import create_module
  from pytext.models.output_layers import ClassificationOutputLayer, OutputLayerBase, PairwiseCosineDistanceOutputLayer
  from pytext.models.representations.bilstm_doc_attention import BiLSTMDocAttention
  from pytext.models.representations.docnn import DocNNRepresentation
  from pytext.models.representations.representation_base import RepresentationBase
  from pytext.utils.file_io import PathManager
  from scipy.special import comb
label_desc: Use image node_id 6 for calling the BasePairwiseModel obj's underlying member method code with example usage: obj._encode_relations(cls, encodings) and returns: encodings