docstr: Using this PyTorch model to make predictions on the data.
  
  :param data: An instance of SparkXShards or a Spark DataFrame
  :param batch_size: Total batch size for all workers used for inference. Each worker's batch
         size would be this value divide the total number of workers. Default is 32.
  :param profile: Boolean. Whether to return time stats for the training procedure.
         Default is False.
  :param feature_cols: feature column names if data is a Spark DataFrame.
  :param output_cols: Column name(s) of the model output data. Only used when data is
         a Spark DataFrame, note the order of column name(s) should be consistent with the
         model output data. Default: None.
  :return: A SparkXShards that contains the predictions with key "prediction" in each shard
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code: def predict(
      self,
      data: Union["SparkXShards", "SparkDataFrame"],
      batch_size: int = 32,
      feature_cols: Optional[List[str]] = None,
      output_cols: Optional[List[str]] = None,
      profile: bool = False,
      callbacks: Optional[List["Callback"]] = None,
  ) -> Union["SparkXShards", "SparkDataFrame"]:
      """
      Using this PyTorch model to make predictions on the data.
  
      :param data: An instance of SparkXShards or a Spark DataFrame
      :param batch_size: Total batch size for all workers used for inference. Each worker's batch
             size would be this value divide the total number of workers. Default is 32.
      :param profile: Boolean. Whether to return time stats for the training procedure.
             Default is False.
      :param feature_cols: feature column names if data is a Spark DataFrame.
      :param output_cols: Column name(s) of the model output data. Only used when data is
             a Spark DataFrame, note the order of column name(s) should be consistent with the
             model output data. Default: None.
      :return: A SparkXShards that contains the predictions with key "prediction" in each shard
      """
      invalidInputError(
          isinstance(batch_size, int) and batch_size > 0,
          "batch_size should be a positive integer",
      )
      batch_size = (
          batch_size // self.num_workers
      )  # Local batch size for each worker
      if batch_size <= 0:
          batch_size = 1
  
      if self.model_creator is None:
          invalidInputError(
              False,
              "Must provide callable function for model_creator "
              "or load a saved model.",
          )
  
      sc = OrcaContext.get_spark_context()
      state_dict = self._get_broadcasted_state_dict(sc)
  
      init_params = dict(mode="predict", state_dict=state_dict)
      init_params.update(self.worker_init_params)
  
      callbacks = callbacks or []
      make_only_mainCallback(callbacks)
      if self.use_tqdm:
          callbacks.append(TqdmCallback())
  
      params = dict(
          batch_size=batch_size, profile=profile, callbacks=callbacks
      )
  
      if isinstance(
          data, DataFrame
      ):  # Computation would be triggered by the user
          xshards, _ = dataframe_to_xshards(
              data,
              validation_data=None,
              feature_cols=feature_cols,
              label_cols=None,
              mode="predict",
              shard_size=batch_size,
          )
  
          pred_shards = self._predict_spark_xshards(
              xshards, init_params, params
          )
          result = convert_predict_xshards_to_dataframe(
              data, pred_shards, output_cols=output_cols
          )
      elif isinstance(
          data, SparkXShards
      ):  # Computation triggered when updating XShards
          xshards = data.to_lazy()
          if xshards._get_class_name() == "pandas.core.frame.DataFrame":
              xshards = process_xshards_of_pandas_dataframe(
                  xshards, feature_cols
              )
              pred_shards = self._predict_spark_xshards(
                  xshards, init_params, params
              )
              # Should add to the original SparkXShards of Pandas DataFrames
              result = add_predict_to_pd_xshards(data, pred_shards)
          else:
              pred_shards = self._predict_spark_xshards(
                  xshards, init_params, params
              )
              result = update_predict_xshards(data, pred_shards)
          # Uncache the original data since it is already included in the result
          data.uncache()
      else:
          invalidInputError(
              False,
              "Only XShards or Spark DataFrame are supported for predict",
          )
  
      return result
  
imports: import types
  import torch
  import copy
  import os
  import shutil
  import tempfile
  import logging
  import math
  from pyspark.sql.dataframe import DataFrame
  from bigdl.dllib.utils.file_utils import is_local_path
  from bigdl.orca.learn.pytorch.pytorch_pyspark_worker import PytorchPysparkWorker
  from bigdl.orca.learn.pytorch.utils import process_stats
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, make_data_creator, update_predict_xshards, process_xshards_of_pandas_dataframe, add_predict_to_pd_xshards
  from bigdl.orca.data import SparkXShards
  from bigdl.orca import OrcaContext
  from bigdl.orca.learn.base_estimator import BaseEstimator
  from bigdl.orca.data.file import get_remote_file_to_local, put_local_file_to_remote
  from bigdl.dllib.utils.common import get_node_and_core_number
  from bigdl.orca.learn.log_monitor import start_log_server, stop_log_server
  from bigdl.orca.learn.pytorch.callbacks.maincallback import make_only_mainCallback
  from bigdl.orca.learn.pytorch.callbacks.tqdm import TqdmCallback, is_tqdm_exists
  from bigdl.orca.learn.pytorch.callbacks.maxsteps import MaxstepsCallback
  from bigdl.orca.learn.utils import find_free_port, find_ip_and_free_port
  from bigdl.dllib.utils.utils import get_node_ip
  from bigdl.dllib.utils.log4Error import invalidInputError
  from typing import TYPE_CHECKING, Union, Optional, Callable, Dict, List
  from bigdl.orca.learn.pytorch.callbacks import Callback
label_desc: Use image node_id 8 for calling the PyTorchPySparkEstimator obj's underlying member method code with example usage: obj.predict(data, batch_size, feature_cols, output_cols, profile, callbacks) and returns: result