docstr: Using this PyTorch model to make predictions on the data.
  
  :param data: An instance of SparkXShards or a Spark DataFrame
  :param batch_size: The number of samples per batch for each worker. 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.
  :return: A SparkXShards that contains the predictions with key "prediction" in each shard
docstr_tok: ["Using","this","PyTorch","model","to","make","predictions","on","the","data",".",":","param","data",":","An","instance","of","SparkXShards","or","a","Spark","DataFrame",":","param","batch_size",":","The","number","of","samples","per","batch","for","each","worker",".","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",".",":","return",":","A","SparkXShards","that","contains","the","predictions","with","key","``","prediction","''","in","each","shard"]
code: def predict(
      self, data, batch_size=32, feature_cols=None, profile=False
  ):
      """
      Using this PyTorch model to make predictions on the data.
  
      :param data: An instance of SparkXShards or a Spark DataFrame
      :param batch_size: The number of samples per batch for each worker. 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.
      :return: A SparkXShards that contains the predictions with key "prediction" in each shard
      """
      return self.estimator.predict(
          data,
          batch_size=batch_size,
          feature_cols=feature_cols,
          profile=profile,
      )
  
imports: from zoo.common.utils import enable_multi_fs_load, enable_multi_fs_save
  from zoo.orca.data.utils import row_to_sample, xshard_to_sample
  from zoo.orca.learn.utils import convert_predict_rdd_to_dataframe, bigdl_metric_results_to_dict, process_xshards_of_pandas_dataframe
  from zoo.pipeline.estimator.estimator import Estimator
  from zoo.orca.learn.ray_estimator import Estimator
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.learn.optimizers import Optimizer, SGD
  from zoo.orca.learn.metrics import Accuracy
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch, OptimMethod
  from zoo.feature.common import FeatureSet
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from pyspark.sql import DataFrame
  import warnings
  import torch
  import types
label_desc: Use image node_id 3 for calling the PyTorchRayEstimator obj's underlying member method code with example usage: obj.predict(data, batch_size, feature_cols, profile) and returns: self