code: def predict(self, ctx, test_data) -> DataFrame:
      if ctx.is_on_guest:
          pred_df = test_data.create_frame(
              with_label=True, with_weight=False
          )
          if self.ovr:
              pred_score = test_data.create_frame(
                  with_label=False, with_weight=False
              )
              for i, class_ctx in ctx.sub_ctx("class").ctxs_range(
                  len(self.labels)
              ):
                  estimator = self.estimator[i]
                  pred = estimator.predict(class_ctx, test_data)
                  pred_score[str(self.labels[i])] = pred
              pred_df[
                  predict_tools.PREDICT_SCORE
              ] = pred_score.apply_row(lambda v: [list(v)])
              predict_result = predict_tools.compute_predict_details(
                  pred_df,
                  task_type=predict_tools.MULTI,
                  classes=self.labels,
              )
          else:
              predict_score = self.estimator.predict(ctx, test_data)
              pred_df[predict_tools.PREDICT_SCORE] = predict_score
              predict_result = predict_tools.compute_predict_details(
                  pred_df,
                  task_type=predict_tools.BINARY,
                  classes=self.labels,
                  threshold=self.threshold,
              )
          return predict_result
      else:
          if self.ovr:
              for i, class_ctx in ctx.sub_ctx("class").ctxs_range(
                  self.label_count
              ):
                  estimator = self.estimator[i]
                  estimator.predict(class_ctx, test_data)
          else:
              self.estimator.predict(ctx, test_data)
  
imports: import logging
  import torch
  from fate.arch import Context, dataframe
  from fate.arch.dataframe import DataFrame
  from fate.arch.protocol.mpc.nn.sshe.lr_layer import SSHELogisticRegressionLayer, SSHELogisticRegressionLossLayer, SSHEOptimizerSGD
  from fate.ml.abc.module import Module, HeteroModule
  from fate.ml.utils import predict_tools
  from fate.ml.utils._convergence import converge_func_factory
  from fate.ml.utils._model_param import get_initialize_func
  from fate.ml.utils._model_param import serialize_param, deserialize_param
label_desc: Use image node_id 7 for calling the SSHELogisticRegression obj's underlying member method code with example usage: obj.predict(ctx, test_data) and returns: predict_result