code: def joint_forward(self, x):
      # x: T x B x C
      if self.joint_layer_norm:
          x = self.joint_layer_norm(x.transpose(0, 1))
          x = x.transpose(0, 1)
  
      for layer in self.joint_layers:
          x, _ = layer(x, self_attn_padding_mask=None)
      return x
  
imports: from dataclasses import dataclass, field
  import os
  import torch
  import torch.nn
  from fairseq import utils
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.models.roberta.model import RobertaClassificationHead
  from fairseq.modules import LayerNorm, TransformerSentenceEncoder, TransformerSentenceEncoderLayer
label_desc: Use image node_id 4 for calling the BertRanker obj's underlying member method code with example usage: obj.joint_forward(x) and returns: x