class_docstr: A Predictor for inference against DJL Model Endpoints.
  
  This is able to serialize Python lists, dictionaries, and numpy arrays to
  multidimensional tensors for DJL inference.
docstr: Initialize a ``DJLPredictor``
  
  Args:
      endpoint_name (str): The name of the endpoint to perform inference
          on.
      sagemaker_session (sagemaker.session.Session): Session object that
          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 json format.
      deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
          Default parses the response from json format to dictionary.
      component_name (str): Optional. Name of the Amazon SageMaker inference
          component corresponding the predictor.
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code: def __init__(
      self,
      endpoint_name: str,
      sagemaker_session: Session = None,
      serializer: BaseSerializer = JSONSerializer(),
      deserializer: BaseDeserializer = JSONDeserializer(),
      component_name=None,
  ):
      """Initialize a ``DJLPredictor``
  
      Args:
          endpoint_name (str): The name of the endpoint to perform inference
              on.
          sagemaker_session (sagemaker.session.Session): Session object that
              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 json format.
          deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
              Default parses the response from json format to dictionary.
          component_name (str): Optional. Name of the Amazon SageMaker inference
              component corresponding the predictor.
      """
      super(DJLPredictor, self).__init__(
          endpoint_name,
          sagemaker_session,
          serializer=serializer,
          deserializer=deserializer,
          component_name=component_name,
      )
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os.path
  import urllib.request
  from json import JSONDecodeError
  from urllib.error import HTTPError, URLError
  from enum import Enum
  from typing import Optional, Union, Dict, Any, List
  import sagemaker
  from sagemaker import s3, Predictor, image_uris, fw_utils
  from sagemaker.deserializers import JSONDeserializer, BaseDeserializer
  from sagemaker.djl_inference import defaults
  from sagemaker.model import FrameworkModel
  from sagemaker.s3_utils import s3_path_join
  from sagemaker.serializers import JSONSerializer, BaseSerializer
  from sagemaker.session import Session
  from sagemaker.utils import _tmpdir, _create_or_update_code_dir, format_tags
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.estimator import Estimator
  from sagemaker.s3 import S3Uploader
label_desc: Use image node_id 1 to create a new DJLPredictor object from inherited base classes: Predictor with example: obj = DJLPredictor(endpoint_name, sagemaker_session, serializer, deserializer, component_name)