code: def forward(self, x, labels=None):
      if len(self.models) == 1:
          linear_out = self.models[0](x)
      else:
          linear_out = t.cat([model(x) for model in self.models], dim=1)
  
      ret_dict = {}
      linear_out = self.sigmoid(linear_out).reshape(
          (-1, len(self.models))
      )
  
      if not self.training:
          if len(self.models) > 1:
              linear_out = self.softmax(linear_out)
  
      ret_dict["pred"] = linear_out
  
      if labels is not None:
          loss = homo_lr_loss(linear_out, labels, dim=len(self.models))
          if self.l1 != 0:
              l1_regularization = t.tensor(0.0)
              for param in self.models.parameters():
                  l1_regularization += t.norm(param, 1)
              loss += self.l1 * l1_regularization
          ret_dict["loss"] = loss
  
      return ret_dict
  
imports: import torch.nn
  from fate.arch.dataframe import DataFrame
  from fate.ml.abc.module import HomoModule
  from fate.ml.utils.model_io import ModelIO
  from fate.arch import Context
  import logging
  import torch
  from fate.ml.nn.homo.fedavg import FedAVGClient, TrainingArguments, FedAVGArguments
  from transformers import default_data_collator
  import functools
  import tempfile
  from fate.ml.utils.predict_tools import array_to_predict_df
  from fate.ml.utils.predict_tools import MULTI, BINARY
  from fate.ml.nn.dataset.table import TableDataset
  from fate.ml.utils._optimizer import optimizer_factory, lr_scheduler_factory
label_desc: Use image node_id 2 for calling the HomoLRModel obj's underlying member method code with example usage: obj.forward(x, labels) and returns: ret_dict