code: def centralized_compute_gradient(self, batch_ctx, encryptor, w, X):
      h = X.shape[0]
      Xw_h = 0.25 * torch.matmul(X, w.detach())
      batch_ctx.guest.put(
          "Xw_h", encryptor.encrypt_tensor(Xw_h, obfuscate=True)
      )
  
      d = batch_ctx.guest.get("d")
      if self.floating_point_precision:
          g = torch.matmul(
              torch.encode_as_int_f(X.T, self.floating_point_precision),
              d,
          )
          g = 1 / (h * self._fixpoint_precision) * g
      else:
          g = 1 / h * torch.matmul(X.T, d)
      return g
  
imports: import logging
  import torch
  from fate.arch import Context
  from fate.arch.dataframe import DataLoader
  from fate.ml.abc.module import HeteroModule
  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 3 for calling the CoordinatedLREstimatorHost obj's underlying member method code with example usage: obj.centralized_compute_gradient(batch_ctx, encryptor, w, X) and returns: g