docstr: Use a model to predict for classes. By default, label predictions start from 0.
  
  # Arguments
  x: Prediction data. A Numpy array or RDD of Sample.
  batch_per_partition:
    The default value is 4.
    When distributed is True, the total batch size is batch_per_thread * rdd.getNumPartitions.
    When distributed is False the total batch size is batch_per_thread * numOfCores.
  zero_based_label: Boolean. Whether result labels start from 0.
                    Default is True. If False, result labels start from 1.
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code: def predict_classes(
      self, x, batch_per_thread=4, zero_based_label=True
  ):
      """
      Use a model to predict for classes. By default, label predictions start from 0.
  
      # Arguments
      x: Prediction data. A Numpy array or RDD of Sample.
      batch_per_partition:
        The default value is 4.
        When distributed is True, the total batch size is batch_per_thread * rdd.getNumPartitions.
        When distributed is False the total batch size is batch_per_thread * numOfCores.
      zero_based_label: Boolean. Whether result labels start from 0.
                        Default is True. If False, result labels start from 1.
      """
      if isinstance(x, np.ndarray):
          data_rdd = to_sample_rdd(x, np.zeros([x.shape[0]]))
      elif isinstance(x, RDD):
          data_rdd = x
      else:
          raise TypeError(
              "Unsupported prediction data type: %s" % type(x)
          )
      return callZooFunc(
          self.bigdl_type,
          "zooPredictClasses",
          self.value,
          data_rdd,
          batch_per_thread,
          zero_based_label,
      )
  
imports: import zoo.pipeline.api.autograd
  from zoo.feature.image import ImageSet
  from zoo.feature.text import TextSet
  from zoo.feature.common import FeatureSet
  from zoo.pipeline.api.keras.base import ZooKerasLayer
  from zoo.pipeline.api.keras.utils import *
  from bigdl.nn.layer import Layer
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 17 for calling the KerasNet obj's underlying member method code with example usage: obj.predict_classes(x, batch_per_thread, zero_based_label) and returns: callZooFunc