code: def predict(self, ctx, test_data):
      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
  
imports: import logging
  import numpy
  import torch
  from fate.arch import Context, dataframe
  from fate.ml.abc.module import HeteroModule
  from fate.ml.utils import predict_tools
  from fate.ml.utils._model_param import check_overflow, deserialize_param, initialize_param, serialize_param
  from fate.ml.utils._optimizer import LRScheduler, Optimizer
label_desc: Use image node_id 5 for calling the CoordinatedLRModuleGuest obj's underlying member method code with example usage: obj.predict(ctx, test_data) and returns: predict_result