code: def trace(self, inputs):
      return torch.jit.trace(self, inputs)
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from pytext.common.constants import Stage, TORCH_VERSION
  from pytext.config import ConfigBase
  from pytext.config.component import create_loss
  from pytext.data.roberta_tensorizer import RoBERTaTensorizer
  from pytext.data.tensorizers import FloatListTensorizer, LabelTensorizer, Tensorizer
  from pytext.loss import BinaryCrossEntropyLoss, MultiLabelSoftMarginLoss
  from pytext.models.decoders.mlp_decoder_two_tower import MLPDecoderTwoTower
  from pytext.models.model import BaseModel
  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.roberta import RoBERTaEncoder, RoBERTaEncoderBase
  from pytext.torchscript.module import ScriptPyTextTwoTowerModuleWithDense, ScriptPyTextTwoTowerModuleWithDenseTower
  from pytext.utils.label import get_label_weights
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 1 for calling the TwoTowerClassificationModel obj's underlying member method code with example usage: obj.trace(inputs) and returns: torch