docstr: Predict the input data
  
  :param data: predict input data.  It can be XShards, Spark DataFrame or
         orca.data.tf.data.Dataset. If data is XShards, each partition can be a Pandas
         DataFrame or a dictionary of {'x': feature}, where feature is a numpy array or a
         tuple of numpy arrays.
  :param batch_size: Total batch size for all workers used for evaluation. Each worker's batch
         size would be this value divide the total number of workers. Default: 32.
  :param verbose: Prints output of one model if true.
  :param steps: Total number of steps (batches of samples) before declaring the prediction
         round finished. Ignored with the default value of None.
  :param callbacks: List of Keras compatible callbacks to apply during prediction.
  :param data_config: An optional dictionary that can be passed to data creator function.
  :param feature_cols: Feature column name(s) of data. Only used when data is a Spark
         DataFrame or an XShards of Pandas DataFrame. Default: None.
  :param min_partition_num: Int. An optional param for repartition the input data when data
         is an **orca.data.tf.data.Dataset**. If min_partition_num != None, the input data
         will be repartitioned to max(min_partition_num, worker_num) partitions. This
         parameter is usually used to improve the prediction performance when the model is a
         customized Keras model, and the number of input partitions is significantly larger
         than the number of workers. Note that if you set this parameter, the order of the
         prediction results is not guaranteed to be the same as the input order, so you need
         to add id information to the input to identify the corresponding prediction results.
         Default: None.
  :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:
code: def predict(
      self,
      data: Union["SparkXShards", "SparkDataFrame", "TFDataset"],
      batch_size: Optional[int] = 32,
      verbose: Union[str, int] = 1,
      steps: Optional[int] = None,
      callbacks: Optional[List["Callback"]] = None,
      data_config: Optional[Dict] = None,
      feature_cols: Optional[List[str]] = None,
      min_partition_num: Optional[int] = None,
      output_cols: Optional[List[str]] = None,
  ) -> Union["SparkXShards", "SparkDataFrame"]:
      """
      Predict the input data
  
      :param data: predict input data.  It can be XShards, Spark DataFrame or
             orca.data.tf.data.Dataset. If data is XShards, each partition can be a Pandas
             DataFrame or a dictionary of {'x': feature}, where feature is a numpy array or a
             tuple of numpy arrays.
      :param batch_size: Total batch size for all workers used for evaluation. Each worker's batch
             size would be this value divide the total number of workers. Default: 32.
      :param verbose: Prints output of one model if true.
      :param steps: Total number of steps (batches of samples) before declaring the prediction
             round finished. Ignored with the default value of None.
      :param callbacks: List of Keras compatible callbacks to apply during prediction.
      :param data_config: An optional dictionary that can be passed to data creator function.
      :param feature_cols: Feature column name(s) of data. Only used when data is a Spark
             DataFrame or an XShards of Pandas DataFrame. Default: None.
      :param min_partition_num: Int. An optional param for repartition the input data when data
             is an **orca.data.tf.data.Dataset**. If min_partition_num != None, the input data
             will be repartitioned to max(min_partition_num, worker_num) partitions. This
             parameter is usually used to improve the prediction performance when the model is a
             customized Keras model, and the number of input partitions is significantly larger
             than the number of workers. Note that if you set this parameter, the order of the
             prediction results is not guaranteed to be the same as the input order, so you need
             to add id information to the input to identify the corresponding prediction results.
             Default: None.
      :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:
      """
      # Use the local batch size for each worker to convert to XShards
      if batch_size:
          invalidInputError(
              isinstance(batch_size, int) and batch_size > 0,
              "batch_size should be a positive integer",
          )
          local_batch_size = batch_size // self.num_workers
          if local_batch_size <= 0:
              local_batch_size = 1
      else:
          local_batch_size = None
      logger.info("Starting predict step.")
      params = dict(
          verbose=verbose,
          batch_size=batch_size,
          steps=steps,
          callbacks=callbacks,
          data_config=data_config,
          output_cols=output_cols,
      )
      from bigdl.orca.data import SparkXShards
      from pyspark.sql import DataFrame
      from bigdl.orca.data.tf.data import Dataset
  
      if isinstance(data, DataFrame):
          xshards, _ = dataframe_to_xshards(
              data,
              validation_data=None,
              feature_cols=feature_cols,
              label_cols=None,
              mode="predict",
              accept_str_col=True,
              shard_size=local_batch_size,
          )
          pred_shards = self._predict_spark_xshards(xshards, params)
          result = convert_predict_xshards_to_dataframe(
              data, pred_shards, output_cols
          )
      elif isinstance(data, SparkXShards):
          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, params)
              result = add_predict_to_pd_xshards(data, pred_shards)
          else:
              pred_shards = self._predict_spark_xshards(xshards, params)
              result = update_predict_xshards(data, pred_shards)
      elif isinstance(data, Dataset):
          data = data.get_xshards()
          if min_partition_num:
              partition_num = max(min_partition_num, self.num_workers)
              if data.num_partitions() != partition_num:
                  data = data.repartition(partition_num)
          pred_shards = self._predict_spark_xshards(data, params)
          result = update_predict_xshards(data, pred_shards)
      else:
          invalidInputError(
              False,
              "Only xshards, Spark DataFrame or orca TF Dataset are supported "
              "for predict",
          )
  
      return result
  
imports: import os
  import types
  from bigdl.orca.common import SafePickle
  import shutil
  import tempfile
  import logging
  import ray
  from bigdl.dllib.utils import log4Error
  from bigdl.dllib.utils.file_utils import is_local_path
  from bigdl.orca.data.file import enable_multi_fs_save, enable_multi_fs_load
  from bigdl.orca.data.ray_xshards import RayXShards
  from bigdl.orca.learn.dl_cluster import RayDLCluster
  from bigdl.orca.learn.tf2.tf_runner import TFRunner
  from bigdl.orca.learn.ray_estimator import Estimator
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe, make_data_creator, add_predict_to_pd_xshards
  from bigdl.orca.data.file import get_remote_file_to_local, get_remote_dir_to_local, is_file
  from bigdl.orca.data.utils import process_spark_xshards
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca.ray import OrcaRayContext
  from typing import TYPE_CHECKING, Dict, List, Callable, Union, Optional
label_desc: Use image node_id 8 for calling the TensorFlow2Estimator obj's underlying member method code with example usage: obj.predict(data, batch_size, verbose, steps, callbacks, data_config, feature_cols, min_partition_num, output_cols) and returns: result