code: def predict(self, ctx, test_data):
      pred_df = test_data.create_frame(
          with_label=True, with_weight=False
      )
      if self.init_param.get("fit_intercept"):
          test_data["intercept"] = 1.0
      X = test_data.values.as_tensor()
      pred = torch.matmul(X, self.w)
      for h_pred in ctx.hosts.get("h_pred"):
          pred += h_pred
      pred_df[predict_tools.PREDICT_SCORE] = pred
      predict_result = predict_tools.compute_predict_details(
          pred_df, task_type=predict_tools.REGRESSION
      )
      return predict_result
  
imports: import logging
  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 deserialize_param, initialize_param, serialize_param
  from fate.ml.utils._optimizer import LRScheduler, Optimizer
label_desc: Use image node_id 5 for calling the CoordinatedLinREstimatorGuest obj's underlying member method code with example usage: obj.predict(ctx, test_data) and returns: predict_result