code: def predict(self, ctx, test_data):
      if self.init_param.get("fit_intercept"):
          test_data["intercept"] = 1.0
      X = test_data.values.as_tensor()
      # logger.info(f"in predict, w: {self.w}")
      pred = torch.matmul(X, self.w.detach())
      for h_pred in ctx.hosts.get("h_pred"):
          pred += h_pred
      pred = torch.sigmoid(pred)
      return pred
  
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 CoordinatedLREstimatorGuest obj's underlying member method code with example usage: obj.predict(ctx, test_data) and returns: pred