class_docstr: Performs predictions against an MLeap serialized SparkML model.
  
  The implementation of
  :meth:`~sagemaker.predictor.Predictor.predict` in this
  `Predictor` requires a json as input. The input should follow the
  json format as documented.
  
  ``predict()`` returns a csv output, comma separated if the output is a
  list.
class_docstr_tok: ["Performs","predictions","against","an","MLeap","serialized","SparkML","model",".","The","implementation","of",":","meth",":","`","~sagemaker.predictor.Predictor.predict","`","in","this","`","Predictor","`","requires","a","json","as","input",".","The","input","should","follow","the","json","format","as","documented",".","``","predict","(",")","``","returns","a","csv","output",",","comma","separated","if","the","output","is","a","list","."]
docstr: Initializes a SparkMLPredictor which should be used with SparkMLModel.
  
  It is used to perform predictions against SparkML models serialized via MLeap.
  The response is returned in text/csv format which is the default response
  format for SparkML Serving container.
  
  Args:
      endpoint (str): The name of the endpoint to perform inference on.
      sagemaker_session (sagemaker.session.Session): Session object which
          manages interactions with Amazon SageMaker APIs and any other
          AWS services needed. If not specified, the estimator creates one
          using the default AWS configuration chain.
      serializer (sagemaker.serializers.BaseSerializer): Optional. Default
          serializes input data to text/csv.
      component_name (str): Optional. Name of the Amazon SageMaker inference
          component corresponding to the predictor.
docstr_tok: ["Initializes","a","SparkMLPredictor","which","should","be","used","with","SparkMLModel",".","It","is","used","to","perform","predictions","against","SparkML","models","serialized","via","MLeap",".","The","response","is","returned","in","text\/csv","format","which","is","the","default","response","format","for","SparkML","Serving","container",".","Args",":","endpoint","(","str",")",":","The","name","of","the","endpoint","to","perform","inference","on",".","sagemaker_session","(","sagemaker.session.Session",")",":","Session","object","which","manages","interactions","with","Amazon","SageMaker","APIs","and","any","other","AWS","services","needed",".","If","not","specified",",","the","estimator","creates","one","using","the","default","AWS","configuration","chain",".","serializer","(","sagemaker.serializers.BaseSerializer",")",":","Optional",".","Default","serializes","input","data","to","text\/csv",".","component_name","(","str",")",":","Optional",".","Name","of","the","Amazon","SageMaker","inference","component","corresponding","to","the","predictor","."]
code: def __init__(
      self,
      endpoint_name,
      sagemaker_session=None,
      serializer=CSVSerializer(),
      component_name=None,
      **kwargs,
  ):
      """Initializes a SparkMLPredictor which should be used with SparkMLModel.
  
      It is used to perform predictions against SparkML models serialized via MLeap.
      The response is returned in text/csv format which is the default response
      format for SparkML Serving container.
  
      Args:
          endpoint (str): The name of the endpoint to perform inference on.
          sagemaker_session (sagemaker.session.Session): Session object which
              manages interactions with Amazon SageMaker APIs and any other
              AWS services needed. If not specified, the estimator creates one
              using the default AWS configuration chain.
          serializer (sagemaker.serializers.BaseSerializer): Optional. Default
              serializes input data to text/csv.
          component_name (str): Optional. Name of the Amazon SageMaker inference
              component corresponding to the predictor.
      """
      sagemaker_session = sagemaker_session or Session()
      super(SparkMLPredictor, self).__init__(
          endpoint_name=endpoint_name,
          sagemaker_session=sagemaker_session,
          serializer=serializer,
          component_name=component_name,
          **kwargs,
      )
  
file_path: sagemaker-python-sdk/src/sagemaker/sparkml/model.py
imports: from __future__ import absolute_import
  from typing import Union, Optional
  from sagemaker import Model, Predictor, Session, image_uris
  from sagemaker.serializers import CSVSerializer
  from sagemaker.utils import pop_out_unused_kwarg
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 1 to create a new SparkMLPredictor object from inherited base classes: Predictor with example: obj = SparkMLPredictor(endpoint_name, sagemaker_session, serializer, component_name)