code: def predict(self, x):
      if isinstance(x, DataLoader):
          pass
      elif isinstance(x, ndarray):
          x = torch.from_numpy(x)
      else:
          invalidInputError(
              False, f"got unsupported data input type: {type(x)}"
          )
      y_pred_local = self.model(x)
      data_map = {"input": y_pred_local.detach().numpy()}
      response = self.fl_client.predict(data_map)
      return tensor_map_to_ndarray_map(response.data.tensorMap)["pred"]
  
imports: import logging
  from numpy import ndarray
  import torch
  from torch import nn
  from bigdl.ppml.fl.nn.fl_client import FLClient
  from torch.utils.data import DataLoader
  from bigdl.dllib.utils.log4Error import invalidInputError, invalidOperationError
  from bigdl.ppml.fl.nn.nn_client import NNClient
  from bigdl.ppml.fl.nn.utils import file_chunk_generate, tensor_map_to_ndarray_map
  import os
  import tempfile
  from nn_service_pb2 import LoadModelRequest, SaveModelRequest
label_desc: Use image node_id 8 for calling the PytorchEstimator obj's underlying member method code with example usage: obj.predict(x) and returns: tensor_map_to_ndarray_map