docstr: Use a model to do prediction.
docstr_tok: ["Use","a","model","to","do","prediction","."]
code: def predict(
      self, x, batch_per_thread=1, distributed=True, mini_batch=False
  ):
      """
      Use a model to do prediction.
      """
      if isinstance(x, ImageSet):
          results = callZooFunc(
              self.bigdl_type,
              "zooPredict",
              self.value,
              x,
              batch_per_thread,
          )
          return ImageSet(results)
  
      if isinstance(x, TFImageDataset):
          results = callZooFunc(
              self.bigdl_type,
              "zooPredict",
              self.value,
              x.get_prediction_data(),
              x.batch_per_thread,
          )
          return ImageSet(results)
  
      if isinstance(x, TFDataset):
          results = callZooFunc(
              self.bigdl_type,
              "zooPredict",
              self.value,
              x.get_prediction_data(),
          )
          return results.map(
              lambda result: Layer.convert_output(result)
          )
  
      if mini_batch:
          results = callZooFunc(
              self.bigdl_type, "zooPredict", self.value, x
          )
          return results.map(
              lambda result: Layer.convert_output(result)
          )
  
      if distributed:
          if isinstance(x, np.ndarray):
              data_rdd = to_sample_rdd(
                  x, np.zeros([x.shape[0]]), getOrCreateSparkContext()
              )
          elif isinstance(x, RDD):
              data_rdd = x
          else:
              invalidInputError(
                  False,
                  "Unsupported prediction data type: %s" % type(x),
              )
          results = callZooFunc(
              self.bigdl_type,
              "zooPredict",
              self.value,
              data_rdd,
              batch_per_thread,
          )
          return results.map(
              lambda result: Layer.convert_output(result)
          )
      else:
          start_idx = 0
          results = []
          while start_idx < len(x):
              end_idx = min(start_idx + batch_per_thread, len(x))
              results.append(self.forward(x[start_idx:end_idx]))
              start_idx += batch_per_thread
  
          return np.concatenate(results, axis=0)
  
imports: import os
  import sys
  import tempfile
  import numpy
  import six
  from pyspark import RDD
  from bigdl.dllib.nn.layer import Layer
  from bigdl.dllib.utils.file_utils import JTensor
  from bigdl.dllib.nncontext import getOrCreateSparkContext
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.feature.image import ImageSet
  from bigdl.orca.tfpark.tf_dataset import TFImageDataset, TFDataset
  import logging
  from bigdl.dllib.utils.utils import to_sample_rdd
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 3 for calling the TFNet obj's underlying member method code with example usage: obj.predict(x, batch_per_thread, distributed, mini_batch) and returns: ImageSet, ImageSet, results, results, results, np