class_docstr: Pairwise classification model following the pattern of two tensorizers + two
  (usually shared) encoders. Also supports exporting a single tensorizer + encoder
  to produce a sentence embedding.
class_docstr_tok: ["Pairwise","classification","model","following","the","pattern","of","two","tensorizers","+","two","(","usually","shared",")","encoders",".","Also","supports","exporting","a","single","tensorizer","+","encoder","to","produce","a","sentence","embedding","."]
code: def arrange_targets(self, tensor_dict):
      return tensor_dict["labels"]
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  import torch.nn
  from pytext.common.constants import Stage
  from pytext.config.component import create_loss
  from pytext.data.bert_tensorizer import BERTTensorizer, BERTTensorizerBase
  from pytext.data.dense_retrieval_tensorizer import BERTContextTensorizerForDenseRetrieval, PositiveLabelTensorizerForDenseRetrieval
  from pytext.data.tensorizers import FloatListTensorizer, LabelTensorizer, NtokensTensorizer, Tensorizer
  from pytext.loss import BinaryCrossEntropyLoss, MultiLabelSoftMarginLoss
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.model import BaseModel, ModelInputBase
  from pytext.models.module import create_module
  from pytext.models.output_layers import ClassificationOutputLayer
  from pytext.models.output_layers.doc_classification_output_layer import BinaryClassificationOutputLayer, MulticlassOutputLayer, MultiLabelOutputLayer
  from pytext.models.pair_classification_model import BasePairwiseModel
  from pytext.models.representations.huggingface_bert_sentence_encoder import HuggingFaceBertSentenceEncoder
  from pytext.models.representations.huggingface_electra_sentence_encoder import HuggingFaceElectraSentenceEncoder
  from pytext.models.representations.representation_base import RepresentationBase
  from pytext.models.representations.transformer_sentence_encoder_base import TransformerSentenceEncoderBase
  from pytext.torchscript.module import ScriptPyTextEmbeddingModuleIndex, ScriptPyTextEmbeddingModuleWithDenseIndex
  from pytext.torchscript.utils import ScriptBatchInput, squeeze_1d, squeeze_2d
  from pytext.utils.label import get_label_weights
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 5 for calling the _EncoderPairwiseModel obj's underlying member method code with example usage: obj.arrange_targets(tensor_dict) and returns: tensor_dict